Signal & Interactive Systems Lab

@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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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)

Stepanov E., Bayer A. O., Riccardi G.,

The UniTN Discourse Parser in CoNLL 2015 Shared Task (Conference)

2015.

(Abstract | Links | BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing)

Vinciarelli A., Esposito A., Andre’ E., Bonin F., Chetouani M., Cohn F. J., Cristani M., Fuhrmann F., Gilmartin E., Hammal Z., Heylen D., Kaiser R., Koutsombogera M., Potamianos A., Renals S., Riccardi G., Salah A. G.

Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions (Article)

Cognitive Computation, pp. 1-17, April 2015, 2015.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

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)

Bayer A. O. and Riccardi G.

Instance-Based On-Line Language Model Adaptation (Conference)

2013.

(Abstract | Links | BibTeX | Tags: Machine Learning, 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)

Bayer A. O. and Riccardi G.

On-line Adaptation of Semantic Models for Spoken Language Understanding (Conference)

2013.

(Abstract | Links | BibTeX | Tags: Machine Learning, Speech Processing)

Ghosh S., Johansson R., Riccardi G. and Tonelli S.

Improving the Recall of a Discourse Parser by Constraint-Based Postprocessing (Conference)

2012.

(BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing)

Ghosh S., Riccardi G. and Johansson R.

Global Features for Shallow Discourse Parsing (Conference)

2012.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing)

Bayer A. O. and Riccardi G.

Joint Language Models for Automatic Speech Recognition and Understanding (Conference)

2012.

(Abstract | Links | BibTeX | Tags: Machine Learning, 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)

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)

Stepanov E. and Riccardi G.

Detecting General Opinions from Customer Surveys (Conference)

2011.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Natural Language Processing)

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)

Nguyen T. T., Moschitti A. and Riccardi G.

Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing)

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)

Quarteroni S., Dinarelli M. and Riccardi G.

Ontology-Based Grounding of Spoken Language Understanding (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Dinarelli M., Moschitti A. and Riccardi G.

Re-Ranking Models For Spoken Language Understanding (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Dinarelli M., Moschitti A. and Riccardi G.

Concept Segmentation and Labeling for Conversational Speech (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Varges S., Riccardi G., Quarteroni S., Ivanov A. V.

The Exploration/Exploitation Trade-Off in Reinforcement Learning for Dialogue Management (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Varges S., Riccardi G., Quarteroni S., Ivanov A. V. and Roberti P.

Leveraging POMDPs trained with User Simulations and Rule-Based Dialog Management in a SDS (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Griol D., Riccardi G. and Sanchis E.

Learning the Structure of Human-Computer and Human-Human Spoken Conversations (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Sporka A. J., Jakub F. and Riccardi G.

Can Machines Call People?- User Experience While Answering Telephone Calls Initiated by Machine (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Varges S., Riccardi G., Quarteroni S., Ivanov A. V. and Roberti P.

On-Line Strategy Computation in Spoken Dialog Systems (Conference)

2009.

(Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Riccardi G., Mosca N., Roberti P. and Baggia P.

The Voice Multimodal Application Framework (Conference)

2009.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Riccardi G., Baggia P. and Roberti P.

Spoken Dialog Systems: From Theory to Technology (Conference)

2009.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Quarteroni S., Riccardi G. and Dinarelli M.

What's in an Ontology for Spoken Language Understanding (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Dinarelli M., Moschitti A. and Riccardi G.

Re-Ranking Models Based on Small Training Data for Spoken Language Understanding (Conference)

2009.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Sebastian V., Riccardi G. and Quarteroni S.

Persistent Information State in a Data-Centric Architecture (Conference)

2008.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Raymond C. and Riccardi G.

Learning with Noisy Supervision for Spoken Language Understanding (Conference)

2008.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Dinarelli M., Moschitti A., Riccardi G.

Joint Generative And Discriminative Models For Spoken Language Understanding (Conference)

2008.

(BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Bisazza A., Dinarelli M., Quarteroni S., Tonelli S., Moschitti A., Riccardi G.

Semantic Annotations For Conversational Speech: from speech transcriptions to predicate argument structures (Conference)

2008.

(BibTeX | Tags: Machine Learning, Natural Language Processing)

Coppola B., Moschitti A., Tonelli S., Riccardi G.

Automatic FrameNet-Based Annotation of Conversational Speech (Conference)

2008.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing)

De Mori R., Bechet F., Hakkani-Tur D., McTear M., Riccardi G. and Tur G.

Spoken Language Understanding (Article)

IEEE Signal Processing Magazine vol. 25, pp.50-58 ,2008, 2008.

(BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

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.

Generative and Discriminative Algorithms for Spoken Language Understanding (Conference)

2007.

(BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Varges S. and Riccardi G.

A Data-Centric Architecture for Data-Driven Spoken Dialog Systems (Conference)

2007.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Moschitti A., Riccardi G. and Raymond C.

Spoken Language Understanding with Kernels for Syntactic/Semantic Structures (Conference)

2007.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

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)

Riccardi G. and Baggia P.

Spoken Dialog Systems: From Theory to Technology (Article)

Edizione della Normale di Pisa, 2006, 2006.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Coppola B., Moschitti A., Riccardi G.

Shallow Semantic Parsing for Spoken Language Understanding (Conference)

2006.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Gupta N., Tur G., Hakkani-Tur D., Bangalore S., Riccardi G. and Rahim M.

The AT&T Spoken Language Understanding System (Article)

IEEE Trans. on Audio, Speech and Language Processing, volume 14, Issue 1, pp. 213-22, 2006, 2006.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

@article{N.2006,
title = {The AT&T Spoken Language Understanding System},
author = {Gupta N., Tur G., Hakkani-Tur D., Bangalore S., Riccardi G. and Rahim M.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/IEEE-SAP-2005-SLU.pdf},
year = {2006},
date = {2006-01-01},
journal = {IEEE Trans. on Audio, Speech and Language Processing, volume 14, Issue 1, pp. 213-22, 2006},
abstract = {Spoken language understanding (SLU) aims at extracting meaning from natural language speech. Over the past decade, a variety of practical goal-oriented spoken dialog systems have been built for limited domains. SLU in these systems ranges from understanding predetermined phrases through fixed grammars, extracting some predefined named entities, extracting users’ intents for call classification, to combinations of users’ intents and named entities. In this paper, we present the SLU system of VoiceTone ® (a service provided by AT&T where AT&T develops, deploys and hosts spoken dialog applications for enterprise customers). The SLU system includes extracting both intents and the named entities from the users’ utterances. For intent determination, we use statistical classifiers trained from labeled data, and for named entity extraction we use rule-based fixed grammars. The focus of our work is to exploit data and to use machine learning techniques to create scalable SLU systems which can be quickly deployed for new domains with minimal human intervention. These objectives are achieved by 1) using the predicate-argument representation of semantic content of an utterance; 2) extending statistical classifiers to seamlessly integrate hand crafted classification rules with the rules learned from data; and 3) developing an active learning framework to minimize the human labeling effort for quickly building the classifier models and adapting them to changes. We present an evaluation of this system using two deployed applications of VoiceTone},
keywords = {Machine Learning, Natural Language Processing, Speech Processing}
}

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Hakkani-Tur D., Riccardi G. and Tur G.

An Active Approach to spoken Language Processing (Article)

ACM Transactions on Speech and Language Processing, Vol. 3, No. 3, pp 1-31, 2006, 2006.

(Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

@article{D.2006,
title = {An Active Approach to spoken Language Processing},
author = {Hakkani-Tur D., Riccardi G. and Tur G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/acm-tslp-06.pdf},
year = {2006},
date = {2006-01-01},
journal = {ACM Transactions on Speech and Language Processing, Vol. 3, No. 3, pp 1-31, 2006},
abstract = {State of the art data-driven speech and language processing systems require a large amount of human intervention ranging from data annotation to system prototyping. In the traditional supervised passive approach, the system is trained on a given number of annotated data samples and evaluated using a separate test set. Then more data is collected arbitrarily, annotated, and the whole cycle is repeated. In this article, we propose the active approach where the system itself selects its own training data, evaluates itself and re-trains when necessary. We first employ active learning which aims to automatically select the examples that are likely to be the most informative for a given task. We use active learning for both selecting the examples to label and the examples to re-label in order to correct labeling errors. Furthermore, the system automatically evaluates itself using active evaluation to keep track of the unexpected events and decides on-demand to label more examples. The active approach enables dynamic adaptation of spoken language processing systems to unseen or unexpected events for nonstationary input while reducing the manual annotation effort significantly. We have evaluated the active approach with the AT&T spoken dialog system used for customer care applications. In this article, we present our results for both automatic speech recognition and spoken language understanding. Categories and Subject Descriptors: I.2.7 [Artificial Intelligence]: Natural Language Processing—Speech recognition and synthesis; I.5.1 [Pattern Recognition]: Models—Statistical General Terms: Algorithms, Languages, Performance Additional Key Words and Phrases: Passive learning, active learning, adaptive learning, unsupervised learning, active evaluation, spoken language understanding, automatic speech recognition, spoken dialog systems, speech and language processing},
keywords = {Machine Learning, Natural Language Processing, Speech Processing}
}

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Potamianos A., Narayanan S. and Riccardi G.

Adaptive Categorical Understanding for Spoken Dialogue Systems' (Article)

Potamianos A., Narayanan S and Riccardi, G., 2005.

(Abstract | Links | BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Hakkani-Tur D., Tur G., Riccardi G. and Kim H. K.

Error Prediction in Spoken Dialog: from Signal-to-Noise Ratio to Semantic Confidence Scores (Conference)

2005.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Bechet F., Riccardi G. and Hakkani-Tur D.

Mining Spoken dialogue Corpora for system Evaluation and Modeling (Conference)

2005.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, Speech Processing)

Karahan M., Hakkani-Tur D., Riccardi G. and Tur G.

Combining Classifiers for Spoken Language Understanding (Conference)

2005.

(BibTeX | Tags: Machine Learning, Natural Language Processing, Speech Processing)

Riccardi G. and Hakkani-Tur D.

Active Learning: Theory and Applications to Automatic Speech Recognition (Article)

IEEE Trans. on Speech and Audio, vol. 13, n.4 , pp. 504-511, 2005, 2005.

(Abstract | Links | BibTeX | Tags: Machine Learning)

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)

Hakkani-Tur D., Tur G., Rahim M. and Riccardi G.

Unsupervised and Active Learning in Automatic Speech Recognition for Call Classification (Conference)

2004.

(BibTeX | Tags: Language Modeling, Machine Learning, Speech Processing)

Riccardi G. and Hakkani-Tur D.

Active and Unsupervised Learning for Automatic Speech Recognition (Conference)

2003.

(BibTeX | Tags: Machine Learning)

Bechet F., Riccardi G. and Hakkani-Tur D.

Multi-channel Sentence Classification for Spoken Dialogue Modeling (Conference)

2003.

(BibTeX | Tags: Machine Learning, Speech Processing)

Fabbrizio G., Dutton D., Gupta N., Hollister B., Rahim M., Riccardi G., Schapire R. and Schroeter J.

AT&T Help Desk (Conference)

2002.

(BibTeX | Tags: Conversational and Interactive Systems , Machine Learning, 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)

Tur D., Riccardi G. and Gorin A. L.

Active Learning for Automatic Speech Recognition (Conference)

2002.

(BibTeX | Tags: Machine Learning)

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)

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)

Gretter R. and Riccardi G.

On-line learning of language models with word error probability distributions (Conference)

2001.

(BibTeX | Tags: Machine Learning, 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)

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)

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)

Riccardi G.

On-line Learning of Acoustic and Lexical Units for Domain-Independent ASR (Conference)

2000.

(BibTeX | Tags: Machine Learning)

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)

Conkie A., Riccardi G. and Rose R. C.

Prosody recognition from speech utterances using acoustic and linguistic based models of prosodic events (Conference)

1999.

(BibTeX | Tags: Machine Learning, Speech Processing)

Gorin A. L., Petrovska-Delacretaz D., Riccardi G. and Wright J. H.

Learning spoken language without transcription (Conference)

1999.

(BibTeX | Tags: Machine Learning)

Arai K., Wright J. H., Riccardi G. and Gorin A. L.

Grammar fragment acquisition using syntactic and semantic clustering (Conference)

1998.

(BibTeX | Tags: Machine Learning)

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., 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)