Mousavi M., Negro R. and Riccardi G. An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain (Conference) Eighth Italian Conference on Computational Linguistics, 2022. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation) Torres M. J., Ravanelli M., Medina-Devilliers S., Lerner D. M. and Riccardi G. Interpretable SincNet-based Deep Learning for Emotion Recognition in Individuals with Autism (Conference) IEEE Conf. Engineering in Medicine and Biology, Conference, 2021. (Links | BibTeX | Tags: Affective Computing, Autism, Machine Learning, Signal Annotation and Interpretation) Mousavi M., Cervone A., Danieli M. and Riccardi G. Would you like to tell me more? Generating a corpus of psychotherapy dialogues (Conference) NAACL, Workshop on NLP for Medical Conversations 2021. (Links | BibTeX | Tags: Signal Annotation and Interpretation) Danieli M., Ciulli T, Mousavi M. and Riccardi G. A Participatory Design of Conversational Artificial Intelligence Agents for Mental Healthcare (Article) Journal of Medical Internet Research (JMIR) Formative Research Journal, 5 (12), 2021. (Links | BibTeX | Tags: Conversational and Interactive Systems , Signal Annotation and Interpretation) Torres M. J., Clarkson T., Hauschild K., Luhmann C. C., Lerner D. M. and Riccardi G. Facial emotions are accurately encoded in the brains of those with autism: A deep learning approach (Article) Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2021. (Links | BibTeX | Tags: Affective Computing, Autism, Machine Learning, Signal Annotation and Interpretation) Roccabruna G., Cervone A. and Riccardi G. Multifunctional ISO standard Dialogue Act tagging in Italian (Conference) Seventh Italian Conference on Computational Linguistics, 2021. (Links | BibTeX | Tags: Signal Annotation and Interpretation) Tammewar A., Cervone A.,Eva-Maria Messner, Riccardi G. Annotation of Emotion Carriers in Personal Narratives (Proceeding) 2020. (Links | BibTeX | Tags: Affective Computing, Natural Language Processing, Signal Annotation and Interpretation) Marinelli F., Cervone A., Tortoreto G., Stepanov E. A., Di Fabbrizio G., Riccardi G. Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning (Conference) 2019. (Links | BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation) Alam F., Danieli M. and Riccardi G. Annotating and Modeling Empathy in Spoken Conversations (Article) Computer Speech and Language, 50 pp. 40-61, 2018. (Links | BibTeX | Tags: Affective Computing, Discourse, Signal Annotation and Interpretation) Dias R., Conboy M. H., Gabany M. J., Clarke A. L. , Osterweil J. L., Avrunin S. G., Arney D., Goldman M. J., Riccardi G., Yule J. S., Zenati A. M. 2018. (Links | BibTeX | Tags: Health Analytics, Interactive Systems, Machine Learning, Signal Annotation and Interpretation) Mayor Torres Juan M., Stepanov A. E. WI '17 Proceedings of the International Conference on Web Intelligence Pages 939-946, Leipzig, Germany - August 23 - 26, 2017, 2017. (Abstract | Links | BibTeX | Tags: Affective Computing, Interactive Systems, Signal Annotation and Interpretation) Stepanov A. E., Chowdhury A. S., Bayer A. O., Ghosh A., Klasinas I., Calvo M., Sanchis E. and Riccardi G. Language Resources and Evaluation, https://doi.org/10.1007/s10579-017-9396-5 , Springer, 2017, 2017. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Celli F., Riccardi G. and Alam F. Multilevel Annotation of Agreement and Disagreement in Italian News Blogs (Proceeding) Proc. Language Resources and Evaluation Conference , Portroz, 2016, 2016. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Celli F., Stepanov A. E., Poesio M. and Riccardi G. Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters (Proceeding) Proc. PEOPLES Workshop at COLING, Osaka 2016., 2016. (Abstract | Links | BibTeX | Tags: Affective Computing, Machine Learning, Signal Annotation and Interpretation) Chowdhury S. , Stepanov A. E. and Riccardi G. Predicting User Satisfaction from Turn-Taking in Spoken Conversations (Proceeding) Proc. INTERSPEECH, San Francisco, 2016., 2016. (Abstract | Links | BibTeX | Tags: Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems, Signal Annotation and Interpretation, Speech Processing) Mayor J. M., Ghosh A., Stepanov A. E. and Riccardi G. HEAL-T: An Efficient PPG-based Heart-Rate And IBI Estimation Method During Physical Exercise (Proceeding) Proc. EUSIPCO, Budapest, 2016, 2016. (Abstract | Links | BibTeX | Tags: Health Analytics, Signal Annotation and Interpretation) Mayor J. M., Stepanov A. E. and Riccardi G. EEG Semantic Decoding Using Deep Neural Networks (Conference) Workshop on Concepts, Actions and Objects, Rovereto, 2016., 2016. (Links | BibTeX | Tags: Health Analytics, Signal Annotation and Interpretation) Danieli M., Balamurali A. R., Stepanov A. E., Favre B., Bechet F. and Riccardi G. Summarizing Behaviors: An Experiment on the Annotation of Call-Centre Conversations (Proceeding) Proc. Language Resources and Evaluation Conference , Portroz, 2016, 2016. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Stepanov E. and Riccardi G. Sentiment Polarity Classification with Low-level Discourse-based Features (Conference) 2015. (Links | BibTeX | Tags: Natural Language Processing, Signal Annotation and Interpretation) Celli F., Ghosh A., Alam F. and Riccardi G. Information Processing and Management, Nov 2015, 2015. (Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Chowdhury A, Calvo M., Ghosh A., Stepanov A. E., Bayer A. O., Riccardi G., Garcia F. and Sanchis E. Selection and Aggregation Techniques for Crowdsourced Semantic Annotation Task (Conference) 2015. (Abstract | Links | BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Statistical Machine Translation) Bayer A. O. and Riccardi G. Deep Semantic Encodings for Language Modeling (Conference) 2015. (Abstract | Links | BibTeX | Tags: Language Modeling, Signal Annotation and Interpretation, Speech Processing) Chowdhury A,, Danieli M. and Riccardi G. The Role of Speakers and Context in Classifying Competition in Overlapping Speech (Conference) 2015. (Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Signal Annotation and Interpretation) Favre B., Stepanov A. E., Trione J. , Bechet F. and Riccardi G. Call Centre Conversation Summarization: A Pilot Task at Multiling 2015 (Conference) 2015. (Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Signal Annotation and Interpretation) Ghosh A, Mayor Torres J.M., Danieli M. and Riccardi G. Detection of Essential Hypertension with Physiological Signals from Wearable Devices (Conference) 2015. (Abstract | Links | BibTeX | Tags: Health Analytics, Machine Learning, Signal Annotation and Interpretation) Ghosh A, Danieli M. and Riccardi G. Annotation and Prediction of Stress and Workload from Physiological and Inertial Signals (Conference) 2015. (Abstract | Links | BibTeX | Tags: Health Analytics, Machine Learning, Signal Annotation and Interpretation) Chowdhury A, Danieli M. and Riccardi G. Annotating and Categorizing Competition in Overlap Speech (Conference) 2015. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing, Signal Annotation and Interpretation) Parodi S, Riccardi G, Castagnino N, Tortolina L, Maffei M, Zoppoli G, Nencioni A, Ballestrero A, Patrone F. Systems Medicine in Oncology: Signaling-networks modeling and new generation decision-support systems (Book) Methods Molecular Biology, Vol. 1386, Schmitz U and Wolkenhauer O (Eds): Systems Medicine, Springer Science press., 2015. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation) Celli F. and Riccardi G. and Ghosh A. CorEA: Italian News Corpus with Emotions and Agreement (Conference) 2014. (Abstract | Links | BibTeX | Tags: Affective Computing, Natural Language Processing, Signal Annotation and Interpretation) Danieli M. , Riccardi G. and Alam F. Annotation of Complex Emotions in Real-Life Dialogues: The Case of Empathy (Conference) 2014. (Abstract | Links | BibTeX | Tags: Affective Computing, Signal Annotation and Interpretation) Chowdhury S. A. and Riccardi G. Unsupervised Recognition and Clustering of Speech Overlaps in Spoken Conversations (Conference) 2014. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Chowdhury S. A., Ghosh A., Stepanov E., Bayer A. O., Riccardi G. and Klasinas I. Cross-Language Transfer of Semantic Annotation via Targeted Crowdsourcing (Conference) 2014. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Stepanov E. and Riccardi G. Towards Cross-Domain PDTB-Style Discourse Parsing (Conference) 2014. (Abstract | Links | BibTeX | Tags: Natural Language Processing, Signal Annotation and Interpretation) Han S., Dinarelli M., Raymond C., Lefevre F., Lehnen P., De Mori R., Moschitti A., Ney H. and Riccardi G. Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages (Article) IEEE Trans. on Audio, Speech and Language Processing, vol. 19, no. 6, pp. 1569-1583, 2011, 2014. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation, Speech Processing) Riccardi G., Ghosh A., Chowdhury S. A. and Bayer A. O. Motivational Feedback in Crowdsourcing: a Case Study in Speech Transcriptions (Conference) 2013. (Abstract | Links | BibTeX | Tags: Affective Computing, Conversational and Interactive Systems , Machine Learning, Signal Annotation and Interpretation) Stepanov E., Kashkarev I., Bayer A. O., Riccardi G. and Ghosh A. Language Style and Domain Adaptation for Cross-Language Porting (Conference) 2013. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation, Statistical Machine Translation) Dinarelli M., Moschitti A. and Riccardi G. Discriminative Reranking for Spoken Language Understanding (Article) IEEE Trans. on Audio, Speech and Language Processing, vol. 20, no. 2, pp. 526-539, 2012, 2012. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation, Speech Processing) Garcia F., Hurtado L. F., Segarra E., Sanchis E. and Riccardi G. Combining Machine Translation Systems for Spoken Language Understanding Portability (Conference) 2012. (Abstract | Links | BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing, Statistical Machine Translation) Ghosh S., Tonelli S., Riccardi G. and Johansson R. End-to-End Discourse Parser Evaluation (Conference) 2011. (Abstract | Links | BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Quarteroni S., Ivanov A. V. and Riccardi G. Simultaneous Dialog Act Segmentation and Classification from Human-Human Spoken Conversations (Conference) 2011. (Abstract | Links | BibTeX | Tags: Natural Language Processing, Signal Annotation and Interpretation, Speech Processing) Moschitti A., Chu-Carroll J., Patwardhan S., Fan J. and Riccardi G. Using Syntacting and Semantic Structural Kernels for Classifying Definition Questions in Jeopardy! (Conference) 2011. (Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Sara Tonelli S., Riccardi G., Prasad R. and Joshi A. Annotation of Discourse Relations for Conversational Spoken Dialogs (Conference) 2010. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing, Signal Annotation and Interpretation) Dinarelli M., Moschitti A. and Riccardi G. Hypotheses Selection for Re-Ranking semantic Annotation (Conference) 2010. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Ivanov A. V., Riccardi G. Automatic Turn Segmentation in Spoken Conversations (Conference) 2010. (Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation) Nguyen T. T., Moschitti A. and Riccardi G. Kernel-based Reranking for Named-Entity Extraction (Conference) 2010. (Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Dinarelli M., Quarteroni S., Tonelli S., Moschitti A. and Riccardi G. Annotating Spoken Dialogs: from Speech Segments to Dialog Acts and Frame Semantics (Conference) 2009. (Abstract | Links | BibTeX | Tags: Natural Language Processing, Signal Annotation and Interpretation) Baggia P., Cutugno F., Danieli M., Pieraccini R. The Multisite 2009 EVALITA Spoken Dialog System Evaluation (Conference) 2009. (Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Rodríguez K. J., Dipper S., Götze M., Poesio P., Riccardi G., Raymond C., Wisniewska J. Standoff Coordination for Multi-Tool Annotation in a Dialogue Corpus (Conference) 2009. (BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Rodríguez K., Raymond C. and Riccardi G. Active Annotation in the LUNA Italian Corpus of Spontaneous Dialogues (Conference) 2008. (BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Raymond C., Riccardi G., Rodríguez K. J. and Wisniewska J. The LUNA Corpus: an Annotation Scheme for a Multi-domain Multi-lingual Dialogue Corpus (Conference) 2007. (BibTeX | Tags: Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Tur G., Hakkani-Tur D. and Riccardi G. Extending Boosting For Call Classification Using Word Confusion Networks (Conference) 2004. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation) Gokhan T., Wright J., Gorin A., Riccardi G., Tur H. Improving Spoken Language Understanding Using Word Confusion Networks (Conference) 2002. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Rochery M., Schapire R., Rahim M., Gupta G., Riccardi G., Bangalore S., Alshawi H. and Douglas S. Combining Prior Knowledge and Boosting for Call Classification in Spoken Language Dialogue (Conference) 2002. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Rahim M., Riccardi G., Saul L., Wright J., Buntschuh B. and Gorin A. L. Robust Numeric Recognition in Spoken Language Dialogue (Article) Speech Communication, 34, pp. 195-212, 2001, 2001. (Abstract | Links | BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Rose R. C., Yao H., Riccardi G. and Wright J. H. Speech Communication, 34, pp. 321-331, 2001, 2001. (Abstract | Links | BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Petrovska-Delacretaz D., Gorin A. L., Riccardi G. and Wright J. H. Detecting Acoustic Morphemes in Lattices for Spoken Language Understanding (Conference) 2000. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Gorin A. L., Wright J. H., Riccardi G., Abella A. and Alonso T. Semantic information processing of spoken language (Conference) 2000. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Potamianos A., Riccardi G. and Narayanan S. Categorical understanding using statistical N-gram models (Conference) 1999. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Rose R. C., Yao H., Riccardi G. and Wright J. Integrating multiple knowledge sources for utterance verification in a large vocabulary speech understanding system (Conference) 1997. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Bocchieri E., Riccardi G. and Anantharaman J. The 1994 AT&T ATIS CHRONUS recognizer (Conference) 1995. (BibTeX | Tags: Language Modeling, Signal Annotation and Interpretation) Bocchieri E., Levin E., Pieraccini R. and Riccardi G. Understanding spontaneous speech (Article) J. of the Italian Assoc. of Artificial Intelligence, Sept. 1995, 1995. (BibTeX | Tags: Machine Learning, Signal Annotation and Interpretation, Speech Processing) Bocchieri E. and Riccardi G. The 1993 AT&T ATIS system (Conference) 1994. (BibTeX | Tags: Language Modeling, Signal Annotation and Interpretation, Speech Processing)2022
title = {An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain},
author = {Mousavi M., Negro R. and Riccardi G.},
year = {2022},
date = {2022-01-27},
publisher = {Eighth Italian Conference on Computational Linguistics},
keywords = {Machine Learning, Signal Annotation and Interpretation}
}
2021
title = {Interpretable SincNet-based Deep Learning for Emotion Recognition in Individuals with Autism},
author = {Torres M. J., Ravanelli M., Medina-Devilliers S., Lerner D. M. and Riccardi G.},
url = {https://arxiv.org/pdf/2107.10790.pdf},
year = {2021},
date = {2021-07-18},
publisher = {IEEE Conf. Engineering in Medicine and Biology, Conference},
keywords = {Affective Computing, Autism, Machine Learning, Signal Annotation and Interpretation}
}
title = {Would you like to tell me more? Generating a corpus of psychotherapy dialogues},
author = {Mousavi M., Cervone A., Danieli M. and Riccardi G.},
url = {https://aclanthology.org/2021.nlpmc-1.1.pdf},
year = {2021},
date = {2021-07-06},
organization = {NAACL, Workshop on NLP for Medical Conversations},
keywords = {Signal Annotation and Interpretation}
}
title = {A Participatory Design of Conversational Artificial Intelligence Agents for Mental Healthcare},
author = {Danieli M., Ciulli T, Mousavi M. and Riccardi G.},
url = {https://formative.jmir.org/2021/12/e30053},
year = {2021},
date = {2021-04-29},
journal = {Journal of Medical Internet Research (JMIR) Formative Research Journal},
volume = {5},
number = {12},
keywords = {Conversational and Interactive Systems , Signal Annotation and Interpretation}
}
title = {Facial emotions are accurately encoded in the brains of those with autism: A deep learning approach},
author = {Torres M. J., Clarkson T., Hauschild K., Luhmann C. C., Lerner D. M. and Riccardi G.},
url = {https://www.sciencedirect.com/science/article/abs/pii/S2451902221001075?via%3Dihub},
year = {2021},
date = {2021-04-16},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
keywords = {Affective Computing, Autism, Machine Learning, Signal Annotation and Interpretation}
}
title = {Multifunctional ISO standard Dialogue Act tagging in Italian},
author = {Roccabruna G., Cervone A. and Riccardi G.},
url = {https://disi.unitn.it/~riccardi/papers2/Clicit20-ISODAItalian.pdf},
year = {2021},
date = {2021-03-01},
publisher = {Seventh Italian Conference on Computational Linguistics},
keywords = {Signal Annotation and Interpretation}
}
2020
title = {Annotation of Emotion Carriers in Personal Narratives},
author = {Tammewar A., Cervone A.,Eva-Maria Messner, Riccardi G.},
editor = {Proc. Language Resources and Evaluation Conference , Marseille*, 2020},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2020/12/LREC20-EmotionCarriers.pdf},
year = {2020},
date = {2020-05-11},
keywords = {Affective Computing, Natural Language Processing, Signal Annotation and Interpretation}
}
2019
title = {Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning },
author = {Marinelli F., Cervone A., Tortoreto G., Stepanov E. A., Di Fabbrizio G., Riccardi G.},
editor = {INTERSPEECH, Graz},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/IS19-Active_Annotation.pdf},
year = {2019},
date = {2019-01-01},
keywords = {Machine Learning, Signal Annotation and Interpretation}
}
2018
title = {Annotating and Modeling Empathy in Spoken Conversations},
author = {Alam F., Danieli M. and Riccardi G.},
url = {https://www.sciencedirect.com/science/article/pii/S088523081730133X},
year = {2018},
date = {2018-07-01},
journal = {Computer Speech and Language},
volume = {50},
pages = {40-61},
keywords = {Affective Computing, Discourse, Signal Annotation and Interpretation}
}
title = {Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care},
author = {Dias R., Conboy M. H., Gabany M. J., Clarke A. L. , Osterweil J. L., Avrunin S. G., Arney D., Goldman M. J., Riccardi G., Yule J. S., Zenati A. M.},
editor = {IEEE Conf. on Cognitive and Computational Aspects of Situation Management, Boston},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/COGSIMA18ContextAwareDashboardSurgicalTeam.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Health Analytics, Interactive Systems, Machine Learning, Signal Annotation and Interpretation}
}
2017
title = {Enhanced face/audio emotion recognition: video and instance level classification using ConvNets and restricted Boltzmann Machines},
author = {Mayor Torres Juan M., Stepanov A. E.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2017/09/ACMWI2017MayorStepanov.pdf
https://dl.acm.org/citation.cfm?id=3109423},
year = {2017},
date = {2017-08-23},
publisher = {WI '17 Proceedings of the International Conference on Web Intelligence Pages 939-946, Leipzig, Germany - August 23 - 26, 2017},
abstract = {Face-based and audio-based emotion recognition modalities have been studied profusely obtaining successful classification rates for arousal/valence levels and multiple emotion categories settings. However, recent studies only focus their attention on classifying discrete emotion categories with a single image representation and/or a single set of audio feature descriptors. Face-based emotion recognition systems use a single image channel representations such as principal-components-analysis whitening, isotropic smoothing, or ZCA whitening. Similarly, audio emotion recognition systems use a standardized set of audio descriptors, including only averaged Mel-Frequency Cepstral coefficients. Both approaches imply the inclusion of decision-fusion modalities to compensate the limited feature separability and achieve high classification rates. In this paper, we propose two new methodologies for enhancing face-based and audio-based emotion recognition based on a single classifier decision and using the EU Emotion Stimulus dataset: (1) A combination of a Convolutional Neural Networks for frame-level feature extraction with a k-Nearest Neighbors classifier for the subsequent frame-level aggregation and video-level classification, and (2) a shallow Restricted Boltzmann Machine network for arousal/valence classification.},
keywords = {Affective Computing, Interactive Systems, Signal Annotation and Interpretation}
}
title = {Cross-Language Transfer of Semantic Annotation via Targeted Crowdsourcing: Task Design and Evaluation},
author = {Stepanov A. E., Chowdhury A. S., Bayer A. O., Ghosh A., Klasinas I., Calvo M., Sanchis E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2017/10/10.1007s10579-017-9396-5.pdf},
year = {2017},
date = {2017-01-01},
journal = {Language Resources and Evaluation, https://doi.org/10.1007/s10579-017-9396-5 , Springer, 2017},
abstract = {Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations.
},
keywords = {Signal Annotation and Interpretation}
}
2016
title = {Multilevel Annotation of Agreement and Disagreement in Italian News Blogs},
author = {Celli F., Riccardi G. and Alam F.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/LREC16-ADR.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. Language Resources and Evaluation Conference , Portroz, 2016},
abstract = {In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels. This is the first resource of this kind in Italian. From the analysis of ADRs at the two levels emerged that agreement annotated at message level is consistent and generally reflected at sentence level, and that the structure of disagreement is more complex. The manual error analysis revealed that this resource is useful not only for the analysis of argumentation, but also for the detection of irony/sarcasm in online debates. The corpus and annotation tool are available for research purposes on request.
},
keywords = {Signal Annotation and Interpretation}
}
title = {Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters},
author = {Celli F., Stepanov A. E., Poesio M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/Coling16PEOPLE-BrexitPaper.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. PEOPLES Workshop at COLING, Osaka 2016.},
abstract = {On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data.
Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.},
keywords = {Affective Computing, Machine Learning, Signal Annotation and Interpretation}
}
Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.
title = {Predicting User Satisfaction from Turn-Taking in Spoken Conversations},
author = {Chowdhury S. , Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/IS16-PredictingUserSatisfactionTurnTaking.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. INTERSPEECH, San Francisco, 2016.},
abstract = {User satisfaction is an important aspect of the user experience while interacting with objects, systems or people. Traditionally user satisfaction is evaluated a-posteriori via spoken or written questionnaires or interviews. In automatic behavioral analysis we aim at measuring the user emotional states and its descriptions as they unfold during the interaction. In our approach, user satisfaction is modeled as the final state of a sequence of emotional states and given ternary values positive, negative, neutral. In this paper, we investigate the discriminating power of turn-taking in predicting user satisfaction in spoken conversations. Turn-taking is used for discourse organization of a conversation by means of explicit phrasing, intonation, and pausing. In this paper, we train different characterization of turn-taking, such as competitiveness of the speech overlaps. To extract turn-taking features we design a turn segmentation and labeling system that incorporates lexical and acoustic information. Given a human-human spoken dialog, our system automatically infers any of the three values of the state of the user satisfaction. We evaluate the classification system on real-life call-center human-human dialogs. The comparative performance analysis shows that the contribution of the turn-taking features outperforms both prosodic and lexical features.},
keywords = {Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems, Signal Annotation and Interpretation, Speech Processing}
}
title = {HEAL-T: An Efficient PPG-based Heart-Rate And IBI Estimation Method During Physical Exercise},
author = {Mayor J. M., Ghosh A., Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/EUSIPCO16-HearRateAlgorithm.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. EUSIPCO, Budapest, 2016},
abstract = {Photoplethysmography (PPG) is a simple, unobtrusive and low-cost technique for measuring blood volume pulse (BVP) used in heart-rate (HR) estimation. However, PPG based heart-rate monitoring devices are often affected by motion artifacts in on-the-go scenarios, and can yield a noisy BVP signal reporting erroneous HR values. Recent studies have proposed spectral decomposition techniques (e.g. M-FOCUSS, Joint-Sparse-Spectrum) to reduce motion artifacts and increase HR estimation accuracy, but at the cost of high computational load. The singular-value-decomposition and recursive calculations present in these approaches are not feasible for the implementation in real-time continuous-monitoring scenarios. In this paper, we propose an efficient HR estimation method based on a combination of fast-ICA, RLS and BHW filter stages that avoids sparse signal reconstruction, while maintaining a high HR estimation accuracy. The proposed method outperforms the state-of-the-art systems on the publicly available TROIKA data set both in terms of HR estimation accuracy (absolute error of 2.25 ± 1.93 bpm) and computational load.
},
keywords = {Health Analytics, Signal Annotation and Interpretation}
}
title = {EEG Semantic Decoding Using Deep Neural Networks},
author = {Mayor J. M., Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/CAOS16-EEGDeepNN.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Workshop on Concepts, Actions and Objects, Rovereto, 2016.},
keywords = {Health Analytics, Signal Annotation and Interpretation}
}
title = {Summarizing Behaviors: An Experiment on the Annotation of Call-Centre Conversations},
author = {Danieli M., Balamurali A. R., Stepanov A. E., Favre B., Bechet F. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/LREC16-Summarization.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. Language Resources and Evaluation Conference , Portroz, 2016},
abstract = {Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry. In this paper we look into inter-annotator agreement of agent behaviour dimensions on two call center corpora. We find that the task can be annotated consistently over time, but that subjectivity issues impacts the quality of the annotation. The reformulation of some of the annotated dimensions is suggested in order to improve agreement.
},
keywords = {Signal Annotation and Interpretation}
}
2015
title = {Sentiment Polarity Classification with Low-level Discourse-based Features},
author = {Stepanov E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/AI-IA15-DiscourseConnect4SenntimentClassification.pdf},
year = {2015},
date = {2015-12-03},
journal = {Proc. CLIC-it, Trento, 2015},
keywords = {Natural Language Processing, Signal Annotation and Interpretation}
}
title = {In the mood for Sharing Contents: Emotions, personality and interaction styles in the diffusion of news},
author = {Celli F., Ghosh A., Alam F. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/IPM15-MoodSharing.pdf},
year = {2015},
date = {2015-11-01},
journal = {Information Processing and Management, Nov 2015},
abstract = {In this paper, we analyze the influence of Twitter users in sharing news articles that may affect the readers’ mood. We collected data of more than 2000 Twitter users who shared news articles from Corriere.it, a daily newspaper that provides mood metadata annotated by readers on a voluntary basis. We automatically annotated personality types and communication styles of Twitter users and analyzed the correlations between personality, communication style, Twitter metadata (such as followig and folllowers) and the type of mood associated to the articles they shared. We also run a feature selection task, to find the best predictors of positive and negative mood sharing, and a classification task. We automatically predicted positive and negative mood sharers with 61.7% F1-measure.},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Selection and Aggregation Techniques for Crowdsourced Semantic Annotation Task},
author = {Chowdhury A, Calvo M., Ghosh A., Stepanov A. E., Bayer A. O., Riccardi G., Garcia F. and Sanchis E.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/IS15-crowdsourcingSelectionAggregation.pdf},
year = {2015},
date = {2015-09-06},
journal = {Proc. INTERSPEECH , Dresden, 2015},
abstract = {Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated.
However, complex tasks like semantic annotation transfer require workers to take simultaneous decisions on chunk segmentation and labeling while acquiring on-the-go domainspecific knowledge. The increased task complexity may generate low judgment agreement and/or poor performance. The goal of this paper is to cope with these crowdsourcing requirements with semantic priming and unsupervised quality control mechanisms. We aim at an automatic quality control that takes into account different levels of workers’ expertise and annotation task performance. We investigate the judgment selection and aggregation techniques on the task of cross-language semantic annotation
transfer. We propose stochastic modeling techniques to estimate the task performance of a worker on a particular judgment with respect to the whole worker group. These estimates are used for the selection of the best judgments as well as weighted consensus-based annotation aggregation. We demonstrate that the technique is useful for increasing the quality of collected annotations.
Index Terms: Crowdsourcing, Annotation, Cross-language porting},
keywords = {Machine Learning, Signal Annotation and Interpretation, Statistical Machine Translation}
}
However, complex tasks like semantic annotation transfer require workers to take simultaneous decisions on chunk segmentation and labeling while acquiring on-the-go domainspecific knowledge. The increased task complexity may generate low judgment agreement and/or poor performance. The goal of this paper is to cope with these crowdsourcing requirements with semantic priming and unsupervised quality control mechanisms. We aim at an automatic quality control that takes into account different levels of workers’ expertise and annotation task performance. We investigate the judgment selection and aggregation techniques on the task of cross-language semantic annotation
transfer. We propose stochastic modeling techniques to estimate the task performance of a worker on a particular judgment with respect to the whole worker group. These estimates are used for the selection of the best judgments as well as weighted consensus-based annotation aggregation. We demonstrate that the technique is useful for increasing the quality of collected annotations.
Index Terms: Crowdsourcing, Annotation, Cross-language porting
title = {Deep Semantic Encodings for Language Modeling},
author = {Bayer A. O. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/IS15-SELMAutoEncoding.pdf},
year = {2015},
date = {2015-09-06},
journal = {Proc. INTERSPEECH , Dresden, 2015},
abstract = {Word error rate (WER) is not an appropriate metric for spoken language systems (SLS) because lower WER does not necessarily yield better understanding performance. Therefore, language models (LMs) that are used in SLS should be trained to jointly optimize transcription and understanding performance. Semantic LMs (SELMs) are based on the theory of frame semantics and incorporate features of frames and meaning bearing words (target words) as semantic context when training LMs.
The performance of SELMs is affected by the errors on the ASR and the semantic parser output. In this paper we address the problem of coping with such noise in the training phase of the neural network-based architecture of LMs. We propose the use of deep autoencoders for the encoding of semantic context while accounting for ASR errors. We investigate the optimization of SELMs both for transcription and understanding by using deep semantic encodings. Deep semantic encodings
suppress the noise introduced by the ASR module, and enable SELMs to be optimized adequately. We assess the understanding performance by measuring the errors made on target words and we achieve 3.7% relative improvement over recurrent neural network LMs.
Index Terms: Language Modeling, Semantic Language Models, Recurrent Neural Networks, Deep Autoencoders},
keywords = {Language Modeling, Signal Annotation and Interpretation, Speech Processing}
}
The performance of SELMs is affected by the errors on the ASR and the semantic parser output. In this paper we address the problem of coping with such noise in the training phase of the neural network-based architecture of LMs. We propose the use of deep autoencoders for the encoding of semantic context while accounting for ASR errors. We investigate the optimization of SELMs both for transcription and understanding by using deep semantic encodings. Deep semantic encodings
suppress the noise introduced by the ASR module, and enable SELMs to be optimized adequately. We assess the understanding performance by measuring the errors made on target words and we achieve 3.7% relative improvement over recurrent neural network LMs.
Index Terms: Language Modeling, Semantic Language Models, Recurrent Neural Networks, Deep Autoencoders
title = {The Role of Speakers and Context in Classifying Competition in Overlapping Speech},
author = {Chowdhury A,, Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/IS15-OverlapClassification.pdf},
year = {2015},
date = {2015-09-06},
journal = {Proc. INTERSPEECH , Dresden, 2015},
abstract = {Overlapping speech is one of the most frequently occurring events in the course of human-human conversations. Understanding the dynamics of overlapping speech is crucial for conversational analysis and for modeling human-machine dialog. Overlapping speech may signal the speaker’s intention to grab the floor with a competitive vs non-competitive act. In this paper, we study the role of speakers, whether they initiate (overlapper) or not (overlappee) the overlap, and the context of the
event. The speech overlap may be explained and predicted by the dialog context, the linguistic or acoustic descriptors. Our goal is to understand whether the competitiveness of the overlap is best predicted by the overlapper, the overlappee, the context or by their combinations. For each overlap and its context we have extracted acoustic, linguistic, and psycholinguistic features and combined decisions from the best classification models. The evaluation of the classifier has been carried out over call center human-human conversations. The results show that the complete knowledge of speakers’ role and context highly contribute to the classification results when using acoustic and
psycholinguistic features. Our findings also suggest that the lexical selections of the overlapper are good indicators of speaker’s competitive or non-competitive intentions.
Index Terms: Spoken Conversation, Automatic Classification, Overlapping Speech, Discourse, Context},
keywords = {Conversational and Interactive Systems , Machine Learning, Signal Annotation and Interpretation}
}
event. The speech overlap may be explained and predicted by the dialog context, the linguistic or acoustic descriptors. Our goal is to understand whether the competitiveness of the overlap is best predicted by the overlapper, the overlappee, the context or by their combinations. For each overlap and its context we have extracted acoustic, linguistic, and psycholinguistic features and combined decisions from the best classification models. The evaluation of the classifier has been carried out over call center human-human conversations. The results show that the complete knowledge of speakers’ role and context highly contribute to the classification results when using acoustic and
psycholinguistic features. Our findings also suggest that the lexical selections of the overlapper are good indicators of speaker’s competitive or non-competitive intentions.
Index Terms: Spoken Conversation, Automatic Classification, Overlapping Speech, Discourse, Context
title = {Call Centre Conversation Summarization: A Pilot Task at Multiling 2015},
author = {Favre B., Stepanov A. E., Trione J. , Bechet F. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/SIGDIAL15-CallCenterConversationSummarizationPilot.pdf},
year = {2015},
date = {2015-09-02},
journal = {Proc. SigDial, Prague, 2015.},
abstract = {This paper describes the results of the Call Centre Conversation Summarization task at Multiling’15. The CCCS task consists in generating abstractive synopses from call centre conversations between a caller and an agent. Synopses are summariesof the problem of the caller, and how it is solved by the agent. Generating them is a very challenging task given that deep analysis of the dialogs and text generation are necessary. Three languages were addressed: French, Italian and English translations
of conversations from those two languages. The official evaluation metric was ROUGE-2. Two participants submitted a total of four systems which had trouble beating the extractive baselines. The
datasets released for the task will allow more research on abstractive dialog summarization.},
keywords = {Conversational and Interactive Systems , Signal Annotation and Interpretation}
}
of conversations from those two languages. The official evaluation metric was ROUGE-2. Two participants submitted a total of four systems which had trouble beating the extractive baselines. The
datasets released for the task will allow more research on abstractive dialog summarization.
title = {Detection of Essential Hypertension with Physiological Signals from Wearable Devices},
author = {Ghosh A, Mayor Torres J.M., Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/EMBC15-HypertensionMonitoringPrediction.pdf},
year = {2015},
date = {2015-08-25},
journal = { Proc. EMBC, IEEE Conf. Engineering in Biology and Medicine Society, Milan, 2015.},
abstract = {Early detection of essential hypertension can support the prevention of cardiovascular disease, a leading cause of death. The traditional method of identification of hypertension involves periodic blood pressure measurement using brachial cuff-based measurement devices. While these devices are noninvasive, they require manual setup for each measurement and they are not suitable for continuous monitoring. Research has shown that physiological signals such as Heart Rate Variability,
which is a measure of the cardiac autonomic activity, is correlated with blood pressure. Wearable devices capable of measuring physiological signals such as Heart Rate, Galvanic Skin Response, Skin Temperature have recently become ubiquitous. However, these signals are not accurate and are prone to noise due to different artifacts. In this paper a) we present a data collection protocol for continuous non-invasive monitoring of physiological signals from wearable devices; b) we implement
signal processing techniques for signal estimation; c) we explore how the continuous monitoring of these physiological signals can be used to identify hypertensive patients; d) We conduct a pilot study with a group of normotensive and hypertensive patients to test our techniques. We show that physiological signals extracted from wearable devices can distinguish between these two groups with high accuracy.},
keywords = {Health Analytics, Machine Learning, Signal Annotation and Interpretation}
}
which is a measure of the cardiac autonomic activity, is correlated with blood pressure. Wearable devices capable of measuring physiological signals such as Heart Rate, Galvanic Skin Response, Skin Temperature have recently become ubiquitous. However, these signals are not accurate and are prone to noise due to different artifacts. In this paper a) we present a data collection protocol for continuous non-invasive monitoring of physiological signals from wearable devices; b) we implement
signal processing techniques for signal estimation; c) we explore how the continuous monitoring of these physiological signals can be used to identify hypertensive patients; d) We conduct a pilot study with a group of normotensive and hypertensive patients to test our techniques. We show that physiological signals extracted from wearable devices can distinguish between these two groups with high accuracy.
title = {Annotation and Prediction of Stress and Workload from Physiological and Inertial Signals},
author = {Ghosh A, Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/EMBC15-StressMonitoringPrediction.pdf},
year = {2015},
date = {2015-08-25},
journal = {Proc. EMBC, IEEE Conf. Engineering in Biology and Medicine Society, Milan, 2015.},
abstract = { Continuous daily stress and high workload can have negative effects on individuals’ physical and mental wellbeing. It has been shown that physiological signals may support the prediction of stress and workload. However, previous research is limited by the low diversity of signals concurring to such predictive tasks and controlled experimental design. In this paper we present 1) a pipeline for continuous and real-life acquisition of physiological and inertial signals 2) a mobile agent application for on-the-go event annotation and 3) an end-to-end signal processing and classification system for
stress and workload from diverse signal streams. We study physiological signals such as Galvanic Skin Response (GSR), Skin Temperature (ST), Inter Beat Interval (IBI) and Blood Volume Pulse (BVP) collected using a non-invasive wearable device; and inertial signals collected from accelerometer and
gyroscope sensors. We combine them with subjects’ inputs (e.g. event tagging) acquired using the agent application, and their emotion regulation scores. In our experiments we explore signal combination and selection techniques for stress and workload prediction from subjects whose signals have been recorded continuously during their daily life. The end-toend classification system is described for feature extraction, signal artifact removal, and classification. We show that a combination of physiological, inertial and user event signals provides accurate prediction of stress for real-life users and signals.},
keywords = {Health Analytics, Machine Learning, Signal Annotation and Interpretation}
}
stress and workload from diverse signal streams. We study physiological signals such as Galvanic Skin Response (GSR), Skin Temperature (ST), Inter Beat Interval (IBI) and Blood Volume Pulse (BVP) collected using a non-invasive wearable device; and inertial signals collected from accelerometer and
gyroscope sensors. We combine them with subjects’ inputs (e.g. event tagging) acquired using the agent application, and their emotion regulation scores. In our experiments we explore signal combination and selection techniques for stress and workload prediction from subjects whose signals have been recorded continuously during their daily life. The end-toend classification system is described for feature extraction, signal artifact removal, and classification. We show that a combination of physiological, inertial and user event signals provides accurate prediction of stress for real-life users and signals.
title = {Annotating and Categorizing Competition in Overlap Speech},
author = {Chowdhury A, Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/ICASSP15-OverlapClassification.pdf},
year = {2015},
date = {2015-04-19},
journal = {Proc. ICASSP, Brisbane, 2015.},
abstract = {Overlapping speech is a common and relevant phenomenon in human conversations, reflecting many aspects of discourse dynamics. In this paper, we focus on the pragmatic role of overlaps in turn-in-progress, where it can be categorized as competitive or non-competitive. Previous studies on these
two categories have mostly relied on controlled scenarios and small datasets. In our study, we focus on call center data, with customers and operators engaged in problem-solving tasks. We propose and evaluate an annotation scheme for these two overlap categories in the context of spontaneous and in-vivo human conversations. We analyze the distinctive predictive characteristics of a very large set of high-dimensional acoustic feature. We obtained a significant improvement in classification results as well as significant reduction in the feature set size.},
keywords = {Discourse, Natural Language Processing, Signal Annotation and Interpretation}
}
two categories have mostly relied on controlled scenarios and small datasets. In our study, we focus on call center data, with customers and operators engaged in problem-solving tasks. We propose and evaluate an annotation scheme for these two overlap categories in the context of spontaneous and in-vivo human conversations. We analyze the distinctive predictive characteristics of a very large set of high-dimensional acoustic feature. We obtained a significant improvement in classification results as well as significant reduction in the feature set size.
title = {Systems Medicine in Oncology: Signaling-networks modeling and new generation decision-support systems},
author = {Parodi S, Riccardi G, Castagnino N, Tortolina L, Maffei M, Zoppoli G, Nencioni A, Ballestrero A, Patrone F.},
year = {2015},
date = {2015-01-01},
publisher = {Methods Molecular Biology, Vol. 1386, Schmitz U and Wolkenhauer O (Eds): Systems Medicine, Springer Science press.},
keywords = {Machine Learning, Signal Annotation and Interpretation}
}
2014
title = {CorEA: Italian News Corpus with Emotions and Agreement},
author = {Celli F. and Riccardi G. and Ghosh A.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/AI-IA14-CoreaAgreementDisagreement-1.pdf},
year = {2014},
date = {2014-11-01},
journal = {Conferenza di Linguistica Computazionale, Pisa, 2014},
abstract = {In this paper, we describe an Italian corpus of news blogs, including bloggers’ emotion tags, and annotations of agreement relations amongst blogger- comment pairs. The main contributions of this work are: the formalization of the agreement relation, the design of guide- lines for its annotation, the quantitative analysis of the annotators’ agreement.},
keywords = {Affective Computing, Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Annotation of Complex Emotions in Real-Life Dialogues: The Case of Empathy},
author = {Danieli M. , Riccardi G. and Alam F.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/AI-IA14-EmpathyCorpusAnnotation.pdf},
year = {2014},
date = {2014-01-01},
journal = {Conferenza di Linguistica Computazionale, Pisa, 2014},
abstract = {In this paper we discuss the problem of an-notating emotions in reallife spoken conversations by investigating the special case of empathy. We propose an annotation model based on the situated theories of emotions. The annotation scheme is directed to ob-serve the natural unfolding of empathy during the conversations. The key component of the protocol is the identification of the annotation unit based both on linguistic and paralinguistic cues. In the last part of the paper we evaluate the reliability of the annotation model.},
keywords = {Affective Computing, Signal Annotation and Interpretation}
}
title = {Unsupervised Recognition and Clustering of Speech Overlaps in Spoken Conversations},
author = {Chowdhury S. A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IS-SLAM14-OverlapUnsuperClustering1.pdf},
year = {2014},
date = {2014-01-01},
journal = {Workshop on Speech, Language and Audio in Multimedia, Penang, Malaysia, 2014},
abstract = {We are interested in understanding speech overlaps and their function in human conversations. Previous studies on speech overlaps have relied on supervised methods, small corpora and controlled conversations. The characterization of overlaps based on timing, semantic and discourse function requires an analysis over a very large feature space. In this study, we discover and characterize speech overlaps using unsupervised techniques. Overlapping segments of human-human spoken conversations were extracted and transcribed using a large vocabulary Automatic Speech Recognizer (ASR). Each overlap instance is automatically projected onto a highdimensional space of acoustic and lexical features. Then, we used unsupervised clustering to discover distinct and wellseparated clusters that may correspond to different discourse functions (e.g., competitive, non-competitive overlap). We have evaluated recognition and clustering algorithms over a large set of real human-human spoken conversations. The automatic system separates two classes of speech overlaps. The clusters have been comparatively evaluated in terms of feature distributions and their contribution to the automatic classification of the clusters.},
keywords = {Signal Annotation and Interpretation}
}
title = {Cross-Language Transfer of Semantic Annotation via Targeted Crowdsourcing},
author = {Chowdhury S. A., Ghosh A., Stepanov E., Bayer A. O., Riccardi G. and Klasinas I.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IS14-CrossLanguageSemanticTransfer-.pdf},
year = {2014},
date = {2014-01-01},
journal = { INTERSPEECH, Singapore, 2014},
abstract = {The development of a natural language speech application requires the process of semantic annotation. Moreover multilingual porting of speech applications increases the cost and complexity of the annotation task. In this paper we address the problem of transferring the semantic annotation of the source language corpus to a low-resource target language via crowdsourcing. The current crowdsourcing approach faces several problems. First, the available crowdsourcing platforms have skewed distribution of language speakers. Second, speech applications require domain-specific knowledge. Third, the lack of reference target language annotation, makes crowdsourcing worker control very difficult. In this paper we address these issues on the task of cross-language transfer of domain-specific semantic annotation from an Italian spoken language corpus to Greek, via targeted crowdsourcing. The issue of domain knowledge transfer is addressed by priming the workers with the source language concepts. The lack of reference annotation is coped with a consensus-based annotation algorithm. The quality of annotation transfer is assessed using source language references and inter-annotator agreement. We demonstrate that the proposed computational methodology is viable and achieves acceptable annotation quality.},
keywords = {Signal Annotation and Interpretation}
}
title = {Towards Cross-Domain PDTB-Style Discourse Parsing},
author = {Stepanov E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/EACL14-PDTBCrossDomain.pdf},
year = {2014},
date = {2014-01-01},
journal = {EACL Workshop on Health Text Mining and Information Analysis, Gothenburg, 2014},
abstract = {Discourse relation parsing is an important task with the goal of understanding text beyond the sentence boundaries. With the availability of annotated corpora (Penn Discourse Treebank) statistical discourse parsers were developed. In the literature it was shown that the discourse parsing subtasks of discourse connective detection and relation sense classification do not generalize well across domains. The biomedical domain is of particular interest due to the availability of Biomedical Discourse Relation Bank (BioDRB). In this paper we present cross-domain evaluation of PDTB trained discourse relation parser and evaluate feature-level domain adaptation techniques on the argument span extraction subtask. We demonstrate that the subtask generalizes well across domains.},
keywords = {Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages},
author = {Han S., Dinarelli M., Raymond C., Lefevre F., Lehnen P., De Mori R., Moschitti A., Ney H. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IEEETSLP10-MultSLU.pdf},
year = {2014},
date = {2014-01-01},
journal = {IEEE Trans. on Audio, Speech and Language Processing, vol. 19, no. 6, pp. 1569-1583, 2011},
abstract = {One of the first steps in building a spoken language understanding (SLU) module for dialogue systems is the extraction of flat concepts out of a given word sequence, usually provided by an automatic speech recognition (ASR) system. In this paper, six different modeling approaches are investigated to tackle the task of concept tagging. These methods include classical, well-known generative and discriminative methods like Finite State Transducers (FSTs), Statistical Machine Translation (SMT), Maximum Entropy Markov Models (MEMMs), or Support Vector Machines (SVMs) as well as techniques recently applied to natural language processing such as Conditional Random Fields (CRFs) or Dynamic Bayesian Networks (DBNs). Following a detailed description of the models, experimental and comparative results are presented on three corpora in different languages and with different complexity. The French MEDIA corpus has already been exploited during an evaluation campaign and so a direct comparison with existing benchmarks is possible. Recently collected Italian and Polish corpora are used to test the robustness and portability of the modeling approaches. For all tasks, manual transcriptions as well as ASR inputs are considered. Additionally to single systems, methods for system combination are investigated. The best performing model on all tasks is based on conditional random fields. On the MEDIA evaluation corpus, a concept error rate of 12.6% could be achieved. Here, additionally to attribute names, attribute values have been extracted using a combination of a rule-based and a statistical approach. Applying system combination using weighted ROVER with all six systems, the concept error rate (CER) drops to 12.0%.},
keywords = {Signal Annotation and Interpretation, Speech Processing}
}
2013
title = {Motivational Feedback in Crowdsourcing: a Case Study in Speech Transcriptions},
author = {Riccardi G., Ghosh A., Chowdhury S. A. and Bayer A. O.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IS13-Crowdsourcing.pdf},
year = {2013},
date = {2013-01-01},
journal = {INTERSPEECH, Lyon, 2013},
abstract = {A widely used strategy in human and machine performance enhancement is achieved through feedback. In this paper we investigate the effect of live motivational feedback on motivating crowds and improving the performance of the crowdsourcing computational model. The provided feedback allows workers to react in real-time and review past actions (e.g. word deletions); thus, to improve their performance on the current and future (sub) tasks. The feedback signal can be controlled via clean (e.g. expert) supervision or noisy supervision in order to trade-off between cost and target performance of the crowd-sourced task. The feedback signal is designed to enable crowd workers to improve their performance at the (sub) task level. The type and performance of feedback signal is evaluated in the context of a speech transcription task. Amazon Mechanical Turk (AMT) platform is used to transcribe speech utterances from different corpora. We show that in both clean (expert) and noisy (worker/turker) real-time feedback conditions the crowd workers are able to provide significantly more accurate transcriptions in a shorter time.},
keywords = {Affective Computing, Conversational and Interactive Systems , Machine Learning, Signal Annotation and Interpretation}
}
title = {Language Style and Domain Adaptation for Cross-Language Porting},
author = {Stepanov E., Kashkarev I., Bayer A. O., Riccardi G. and Ghosh A.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/ASRU13-LangAdaptCrossPorting.pdf},
year = {2013},
date = {2013-01-01},
journal = {IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, 2013},
abstract = {Automatic cross-language Spoken Language Understanding porting is plagued by two limitations. First, SLU are usually trained on limited domain corpora. Second, language pair resources (e.g. aligned corpora) are scarce or unmatched in style (e.g. news vs. conversation). We present experiments on automatic style adaptation of the input for the translation systems and their output for SLU. We approach the problem of scarce aligned data by adapting the available parallel data to the target domain using limited in-domain and larger web crawled close-to-domain corpora. SLU performance is optimized by re-ranking its output with Recurrent Neural Network-based joint language model. We evaluate end-to-end SLU porting on close and distant language pairs: Spanish - Italian and Turkish - Italian; and achieve significant improvements both in translation quality and SLU performance.},
keywords = {Signal Annotation and Interpretation, Statistical Machine Translation}
}
2012
title = {Discriminative Reranking for Spoken Language Understanding},
author = {Dinarelli M., Moschitti A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IEEETSLP11-DRMSLU1.pdf},
year = {2012},
date = {2012-01-01},
journal = {IEEE Trans. on Audio, Speech and Language Processing, vol. 20, no. 2, pp. 526-539, 2012},
abstract = {Spoken language understanding (SLU) is concerned with the extraction of meaning structures from spoken utterances. Recent computational approaches to SLU, e.g., conditional random fields (CRFs), optimize local models by encoding several features, mainly based on simple n-grams. In contrast, recent works have shown that the accuracy of CRF can be significantly improved by modeling long-distance dependency features. In this paper, we propose novel approaches to encode all possible dependencies between features and most importantly among parts of the meaning structure, e.g., concepts and their combination. We rerank hypotheses generated by local models, e.g., stochastic finite state transducers (SFSTs) or CRF, with a global model. The latter encodes a very large number of dependencies (in the form of trees or sequences) by applying kernel methods to the space of all meaning (sub) structures. We performed comparative experiments between SFST, CRF, support vector machines (SVMs), and our proposed discriminative reranking models (DRMs) on representative conversational speech corpora in three different languages: the ATIS (English), the MEDIA (French), and the LUNA (Italian) corpora. These corpora have been collected within three different domain applications of increasing complexity: informational, transactional, and problem-solving tasks, respectively. The results show that our DRMs consistently outperform the state-of-the-art models based on CRF.},
keywords = {Signal Annotation and Interpretation, Speech Processing}
}
title = {Combining Machine Translation Systems for Spoken Language Understanding Portability},
author = {Garcia F., Hurtado L. F., Segarra E., Sanchis E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/SLT12-MTPortSLU.pdf},
year = {2012},
date = {2012-01-01},
journal = {IEEE/ACL Workshop on Spoken Language Technology, Miami, 2012},
abstract = {We are interested in the problem of learning Spoken Language Understanding (SLU) models for multiple target languages. Learning such models requires annotated corpora, and porting to different languages would require corpora with parallel text translation and semantic annotations. In this paper we investigate how to learn a SLU model in a target language starting from no target text and no semantic annotation. Our proposed algorithm is based on the idea of exploiting the diversity (with regard to performance and coverage) of multiple translation systems to transfer statistically stable word-toconcept mappings in the case of the romance language pair, French and Spanish. Each translation system performs differently at the lexical level (wrt BLEU). The best translation system performances for the semantic task are gained from their combination at different stages of the portability methodology. We have evaluated the portability algorithms on the French MEDIA corpus, using French as the source language and Spanish as the target language. The experiments show the effectiveness of the proposed methods with respect to the source language SLU baseline.},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing, Statistical Machine Translation}
}
2011
title = {End-to-End Discourse Parser Evaluation},
author = {Ghosh S., Tonelli S., Riccardi G. and Johansson R.},
url = {https://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6061347&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6061347},
year = {2011},
date = {2011-01-01},
journal = {IEEE International Conference on Semantic Computing, Menlo Park, USA, 2011},
abstract = {We are interested in the problem of discourse parsing of textual documents. We present a novel end-to-end discourse parser that, given a plain text document in input, identifies the discourse relations in the text, assigns them a semantic label and detects discourse arguments spans. The parsing architecture is based on a cascade of decisions supported by Conditional Random Fields (CRF). We train and evaluate three different parsers using the PDTB corpus. The three system versions are compared to evaluate their robustness with respect to deep/shallow and automatically extracted syntactic features.},
keywords = {Discourse, Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Simultaneous Dialog Act Segmentation and Classification from Human-Human Spoken Conversations},
author = {Quarteroni S., Ivanov A. V. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/ICASSP11-DASegmcClass.pdf},
year = {2011},
date = {2011-01-01},
journal = {ICASSP, Prague, 2011},
abstract = {An accurate identification dialog acts (DAs), which represent the illocutionary aspect of communication, is essential to support the understanding of human conversations. This requires 1) the segmentation of human-human dialogs into turns, 2) the intra-turn segmentation into DA boundaries and 3) the classification of each segment according to a DA tag. This process is particularly challenging when both segmentation and tagging are automated and utterance hypotheses derive from the erroneous results of ASR. In this paper, we use Conditional Random Fields to learn models for simultaneous segmentation and labeling of DAs from whole human-human spoken dialogs. We identify the best performing lexical feature combinations on the LUNA and SWITCHBOARD human-human dialog corpora and compare performances to those of discriminative D classifiers based on manually segmented utterances. Additionally, we assess our models’ robustness to recognition errors, showing that DA identification is robust in the presence of high word error rates.},
keywords = {Natural Language Processing, Signal Annotation and Interpretation, Speech Processing}
}
title = {Using Syntacting and Semantic Structural Kernels for Classifying Definition Questions in Jeopardy!},
author = {Moschitti A., Chu-Carroll J., Patwardhan S., Fan J. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/EMNLP11-QuesClassJeopardy.pdf},
year = {2011},
date = {2011-01-01},
journal = {EMNLP, Edinburgh, 2011},
abstract = {The last decade has seen many interesting applications of Question Answering (QA) technology. The Jeopardy! quiz show is certainly one of the most fascinating, from the viewpoints of both its broad domain and the complexity of its language. In this paper, we study kernel methods applied to syntactic/semantic structures for accurate classification of Jeopardy! definition questions. Our extensive empirical analysis shows that our classification models largely improve on classifiers based on word-language models. Such classifiers are also used in the state-of-the-art QA pipeline constituting Watson, the IBM Jeopardy! system. Our experiments measuring their impact on Watson show enhancements in QA accuracy and a consequent increase in the amount of money earned in game-based evaluation.},
keywords = {Conversational and Interactive Systems , Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2010
title = {Annotation of Discourse Relations for Conversational Spoken Dialogs},
author = {Sara Tonelli S., Riccardi G., Prasad R. and Joshi A.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/LREC10-DialoguePDTBAnnotation.pdf},
year = {2010},
date = {2010-01-01},
journal = {LREC Valletta, 2010},
abstract = {In this paper, we make a qualitative and quantitative analysis of discourse relations within the LUNA conversational spoken dialog corpus. In particular, we describe the adaptation of the Penn Discourse Treebank (PDTB) annotation scheme to the LUNA dialogs. We discuss similarities and differences between our approach and the PDTB paradigm and point out the peculiarities of spontaneous dialogs w.r.t. written text, which motivated some changes in the sense hierarchy. Then, we present corpus statistics about the discourse relations within a representative set of annotated dialogs.},
keywords = {Discourse, Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Hypotheses Selection for Re-Ranking semantic Annotation},
author = {Dinarelli M., Moschitti A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/SLT10-HypothSelectionReranking.pdf},
year = {2010},
date = {2010-01-01},
journal = {IEEE/ACL Workshop on Spoken Language Technology, San Francisco, 2010},
abstract = {Discriminative reranking has been successfully used for several tasks of Natural Language Processing (NLP). Recently it has been applied also to Spoken Language Understanding, imrpoving state-of-the-art for some applications. However, such proposed models can be further improved by considering: (i) a better selection of the initial nbest hypotheses to be re-ranked and (ii) the use of a strategy that decides when the reranking model should be used, i.e. in some cases only the basic approach should be applied. In this paper, we apply a semantic inconsistency metric to select the n-best hypotheses from a large set generated by an SLU basic system. Then we apply a state-of-the-art re-ranker based on the Partial Tree Kernel (PTK), which encodes SLU hypotheses in Support Vector Machines (SVM) with complex structured features. Finally, we apply a decision model based on confidence values to select between the first hypothesis provided by the basic SLU model and the first hypothesis provided by the re-ranker. We show the effectiveness of our approach presenting comparative results obtained by reranking hypotheses generated by two very different models: a simple Stochastic Language Model encoded in Finite State Machines (FSM) and a Conditional Random Field (CRF) model. We evaluate our approach on the French MEDIA corpus and on an Italian corpus acquired in the European Project LUNA. The results show a significant improvement with respect to the current state-of-the-art and previous re-ranking models. Index Terms: Spoken Language Understanding, Discriminative Reranking, Kernel Methods.},
keywords = {Signal Annotation and Interpretation}
}
title = {Automatic Turn Segmentation in Spoken Conversations},
author = {Ivanov A. V., Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IS10-AutoTurnSegmentation.pdf},
year = {2010},
date = {2010-01-01},
journal = { INTERSPEECH, Makuhari, 2010},
abstract = {In this paper we have studied the problem of detecting the spoken turn boundaries in human-human spoken conversations. The automation of this task is essential to enable the analysis, recognition and understanding of the speech transcriptions and dialog structures (e.g. turn taking, dialog act segmentation etc.). The problem formulation is different from previous work on metadata extraction in that we work on the time domain for the detection of boundaries. This approach has the advantage of giving fine grain measures of speech events and does not rely on the automatic speech transcriptions. We have explored applicability of different algorithms for this task and have found that a hidden Markov model combining results of the modulation spectrum analysis and Kullback-Leibler divergence of adjacent signal portions produces the best results. The performance of the algorithms has been evaluated on the Switchboard conversational speech corpus.},
keywords = {Signal Annotation and Interpretation}
}
title = {Kernel-based Reranking for Named-Entity Extraction},
author = {Nguyen T. T., Moschitti A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/Coling10-KernelNE.pdf},
year = {2010},
date = {2010-01-01},
journal = {COLING, Bejing, 2010},
abstract = {We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The former features are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model dependencies amongst tagged candidate examples. The experiments on two standard corpora in two languages, i.e. the Italian EVALITA 2009 and the English CoNLL 2003 datasets, show a large improvement on CRFs in F-measure, i.e. from 80.34% to 84.33% and from 84.86% to 88.16%, respectively. Our analysis reveals that both kernels provide a comparable improvement over the CRFs baseline. Additionally, their combination improves CRFs much more than the sum of the individual contributions, suggesting an interesting kernel synergy.},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2009
title = {Annotating Spoken Dialogs: from Speech Segments to Dialog Acts and Frame Semantics},
author = {Dinarelli M., Quarteroni S., Tonelli S., Moschitti A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/EACL09-LUNACorpusAnnotation.pdf},
year = {2009},
date = {2009-01-01},
journal = {EACL Workshop on Semantic Representation of Spoken Language -Athens, 2009},
abstract = {We are interested in extracting semantic structures from spoken utterances generated within conversational systems. Current Spoken Language Understanding systems rely either on hand-written semantic grammars or on flat attribute-value sequence labeling. While the former approach is known to be limited in coverage and robustness, the latter lacks detailed relations amongst attribute-value pairs. In this paper, we describe and analyze the human annotation process of rich semantic structures in order to train semantic statistical parsers. We have annotated spoken conversations from both a human-machine and a human-human spoken dialog corpus. Given a sentence of the transcribed corpora, domain concepts and other linguistic features are annotated, ranging from e.g. part-of-speech tagging and constituent chunking, to more advanced annotations, such as syntactic, dialog act and predicate argument structure. In particular, the two latter annotation layers appear to be promising for the design of complex dialog systems. Statistics and mutual information estimates amongst such features are reported and compared across corpora.},
keywords = {Natural Language Processing, Signal Annotation and Interpretation}
}
title = {The Multisite 2009 EVALITA Spoken Dialog System Evaluation},
author = {Baggia P., Cutugno F., Danieli M., Pieraccini R.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/EVALITA09-MultisiteSDSEvaluation.pdf},
year = {2009},
date = {2009-01-01},
journal = {AI*IA EVALITA Workshop, Brescia, 2009},
abstract = {This document presents the coordination and the evaluation procedures for the Spoken Dialogue System Task in EVALITA 2009. Three institutions participated into the competition, University of Trento, University of Naples and Loquendo. EVALITA participants were asked to develop a SDS application operating in the sales force domain, they were provided with a preliminary list of scenarios indicating system accounting modalities and a possible list of subtasks that should made possible. The three systems were hosted on a server at Trento, 19 volunteers called all of them. The calls have been recorded, transcribed and annotated. The evaluation work, based on scripting run on the annotations, has been mainly focused on assessing performance at the dialogue, task, and concept levels. Detailed results indicating the systems performances are reported in the paper. This document presents the coordination and the evaluation procedures for the Spoken Dialogue System Task in EVALITA 2009. Three institutions participated into the competition, University of Trento, University of Naples and Loquendo SpA.},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
title = {Standoff Coordination for Multi-Tool Annotation in a Dialogue Corpus},
author = {Rodríguez K. J., Dipper S., Götze M., Poesio P., Riccardi G., Raymond C., Wisniewska J.},
year = {2009},
date = {2009-01-01},
journal = {ACL LAW Workshop, Prague, 2007},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2008
title = {Active Annotation in the LUNA Italian Corpus of Spontaneous Dialogues},
author = {Rodríguez K., Raymond C. and Riccardi G.},
year = {2008},
date = {2008-01-01},
journal = {Proc. Language Resources and Evaluation (LREC), Marrakech, 2008},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2007
title = {The LUNA Corpus: an Annotation Scheme for a Multi-domain Multi-lingual Dialogue Corpus},
author = {Raymond C., Riccardi G., Rodríguez K. J. and Wisniewska J.},
year = {2007},
date = {2007-01-01},
journal = {11th Workshop on the Semantics and Pragmatics of Dialogue (DECALOG'07), Rovereto, 2007},
keywords = {Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2004
title = {Extending Boosting For Call Classification Using Word Confusion Networks},
author = {Tur G., Hakkani-Tur D. and Riccardi G.},
year = {2004},
date = {2004-01-01},
journal = {IEEE ICASSP, Montreal, May 2004},
keywords = {Machine Learning, Signal Annotation and Interpretation}
}
2002
title = {Improving Spoken Language Understanding Using Word Confusion Networks},
author = {Gokhan T., Wright J., Gorin A., Riccardi G., Tur H.},
year = {2002},
date = {2002-01-01},
journal = {ICSLP, Denver, 2002},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
title = {Combining Prior Knowledge and Boosting for Call Classification in Spoken Language Dialogue},
author = {Rochery M., Schapire R., Rahim M., Gupta G., Riccardi G., Bangalore S., Alshawi H. and Douglas S.},
year = {2002},
date = {2002-01-01},
journal = {Proc. IEEE ICASSP, Orlando, 2002},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
2001
title = {Robust Numeric Recognition in Spoken Language Dialogue},
author = {Rahim M., Riccardi G., Saul L., Wright J., Buntschuh B. and Gorin A. L.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/numericlang-speechcomm-2001.pdf},
year = {2001},
date = {2001-11-01},
journal = {Speech Communication, 34, pp. 195-212, 2001},
abstract = {This paper addresses the problem of automatic numeric recognition and understanding in spoken language dialogue. We show that accurate numeric understanding in ̄uent unconstrained speech demands maintaining robustness at several dierent levels of system design, including acoustic, language, understanding and dialogue. We describe a robust system for numeric recognition and present algorithms for feature extraction, acoustic and language modeling, discriminative training, utterance veri®cation and numeric understanding and validation. Experimental results from a ®eld-trial of a spoken dialogue system are presented that include customers\' responses to credit card and telephone number requests. Ó 2001 Elsevier Science B.V. All rights reserved.},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
title = {Integration of Utterance Verification with Statistical Language Modeling and Spoken Language Understanding},
author = {Rose R. C., Yao H., Riccardi G. and Wright J. H.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/uv-speechcomm-2001.pdf},
year = {2001},
date = {2001-01-01},
journal = {Speech Communication, 34, pp. 321-331, 2001},
abstract = {Methods for utterance veri®cation (UV) and their integration into statistical language modeling and understanding formalisms for a large vocabulary spoken understanding system are presented. The paper consists of three parts. First, a set of acoustic likelihood ratio (LR) based UV techniques are described and applied to the problem of rejecting portions of a hypothesized word string that may have been incorrectly decoded by a large vocabulary continuous speech recognizer. Second, a procedure for integrating the acoustic level con®dence measures with the statistical language model is described. Finally, the eect of integrating acoustic level con®dence into the spoken language understanding unit (SLU) in a call-type classi®cation task is discussed. These techniques were evaluated on utterances collected from a highly unconstrained call routing task performed over the telephone network. They have been evaluated in terms of their ability to classify utterances into a set of 15 call-types that are accepted by the application. Ó 2001 Elsevier Science B.V. All rights reserved.},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
2000
title = {Detecting Acoustic Morphemes in Lattices for Spoken Language Understanding},
author = {Petrovska-Delacretaz D., Gorin A. L., Riccardi G. and Wright J. H.},
year = {2000},
date = {2000-10-01},
journal = {Proc. ICSLP, Beijing, Oct. 2000},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
title = {Semantic information processing of spoken language},
author = {Gorin A. L., Wright J. H., Riccardi G., Abella A. and Alonso T.},
year = {2000},
date = {2000-10-01},
journal = {ATR Workshop on Multi-Lingual Speech Communication, Oct. 2000},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
1999
title = {Categorical understanding using statistical N-gram models},
author = {Potamianos A., Riccardi G. and Narayanan S.},
year = {1999},
date = {1999-09-01},
journal = {Proc. EUROSPEECH, Budapest, Hungary, Sept. 1999},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
1997
title = {Integrating multiple knowledge sources for utterance verification in a large vocabulary speech understanding system},
author = {Rose R. C., Yao H., Riccardi G. and Wright J.},
year = {1997},
date = {1997-01-01},
journal = {Proc. IEEE ASR Workshop Proc., Santa Barbara, 1997},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
1995
title = {The 1994 AT&T ATIS CHRONUS recognizer},
author = {Bocchieri E., Riccardi G. and Anantharaman J.},
year = {1995},
date = {1995-01-01},
journal = {Proc. 1995 ARPA Spoken Languge Technology Workshop, Austin, Texas, Jan. 1995, pp. 265-268},
keywords = {Language Modeling, Signal Annotation and Interpretation}
}
title = {Understanding spontaneous speech},
author = {Bocchieri E., Levin E., Pieraccini R. and Riccardi G.},
year = {1995},
date = {1995-01-01},
journal = {J. of the Italian Assoc. of Artificial Intelligence, Sept. 1995},
keywords = {Machine Learning, Signal Annotation and Interpretation, Speech Processing}
}
1994
title = {The 1993 AT&T ATIS system},
author = {Bocchieri E. and Riccardi G.},
year = {1994},
date = {1994-01-01},
journal = {Proc. 1994 ARPA Spoken Language Technology Workshop, Plainsboro, NJ, March 1994, pp. 41-42},
keywords = {Language Modeling, Signal Annotation and Interpretation, Speech Processing}
}