Signal & Interactive Systems Lab

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.

Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care (Conference)

2018.

(Links | BibTeX | Tags: Health Analytics, Interactive Systems, Machine Learning, Signal Annotation and Interpretation)

Mayor Torres Juan M., Stepanov A. E.

Enhanced face/audio emotion recognition: video and instance level classification using ConvNets and restricted Boltzmann Machines (Proceeding)

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.

Cross-Language Transfer of Semantic Annotation via Targeted Crowdsourcing: Task Design and Evaluation (Article)

Language Resources and Evaluation, https://doi.org/10.1007/s10579-017-9396-5 , Springer, 2017, 2017.

(Abstract | Links | BibTeX | Tags: Signal Annotation and Interpretation)

@article{E.2017,
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}
}

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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.

In the mood for Sharing Contents: Emotions, personality and interaction styles in the diffusion of news (Article)

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)

@conference{A2015b,
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}
}

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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)

@article{S.2014,
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}
}

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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)

@conference{M2010,
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}
}

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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.

Integration of Utterance Verification with Statistical Language Modeling and Spoken Language Understanding (Article)

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)