Using prosody to classify discourse relations
This work aims to explore the correlation between the discourse structure of a spoken monologue and its prosody by predicting discourse relations from different prosodic attributes. For this purpose, a corpus of semi-spontaneous monologues in English has been automatically annotated according to the...
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| Otros Autores: | , , , , , , , , , , , |
| Formato: | Acta de conferencia Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
International Speech Communication Association
2017
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 09532caa a22010337a 4500 | ||
|---|---|---|---|
| 001 | PAPER-17809 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518204911.0 | ||
| 008 | 190410s2017 xx ||||fo|||| 10| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-85039154207 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Kleinhans, J. | |
| 245 | 1 | 0 | |a Using prosody to classify discourse relations |
| 260 | |b International Speech Communication Association |c 2017 | ||
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Hirschberg, J., Litman, D., Now let's talk about now: Identifying cue phrases intonationally (1987) Proceedings of the 25th Annual Meeting on Association for Computational Linguistics, pp. 163-171. , Association for Computational Linguistics | ||
| 504 | |a Hirschberg, J., Litman, D., Pierrehumbert, J.B., Ward, G., Intonation and the intentional structure of discourse (1987) Proceedings of the 10th International Joint Conference on Artificial Intelligence, 2 (1), pp. 636-639 | ||
| 504 | |a Murray, G., Renals, S., Taboada, M., Prosodic correlates of rhetorical relations Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech, 2006, pp. 1-7. , June | ||
| 504 | |a Mann, W.C., Thompson, S.A., (1988) Rhetorical Structure Theory: Toward A Functional Theory of Text Organization, pp. 243-281 | ||
| 504 | |a Zwicky, A., Clitics and particles (1985) Language, 61 (2), pp. 283-305 | ||
| 504 | |a Fraser, B., What are discourse markers? (1999) Journal of Pragmatics, 31, pp. 931-952 | ||
| 504 | |a Louwerse, M.M., Mitchell, H., Towards a taxonomy of a set of discourse markers in dialog: A theoretical and computational linguistic account (2003) Discourse Processes, 35 (1), pp. 199-239 | ||
| 504 | |a Schiffrin, D., (1988) Discourse Markers, , Cambridge University Press | ||
| 504 | |a Fries, C.C., (1973) The Structure of English: An Introduction to the Construction of English Sentences, , Longman | ||
| 504 | |a Knott, A., Dale, R., Using linguistic phenomena to motivate a set of coherence relations (1994) Discourse Processes, 18, pp. 35-62 | ||
| 504 | |a Taboada, M., Discourse markers as signals (or not) of rhetorical relations (2006) Journal of Pragmatics, 38, pp. 567-592 | ||
| 504 | |a Janin, A., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Morgan, N., Peskin, B., Stolcke, A., The icsi meeting corpus. Acoustics, speech, and signal processing, 2003 (2003) Proceedings.(ICASSP03). 2003 IEEE International Conference on, 1 | ||
| 504 | |a Farrús, M., Lai, C., Moore, J.D., Paragraph-based prosodic cues for speech synthesis applications (2016) Proceedings of the 8th International Conference on Speech Prosody (SP 2016) | ||
| 504 | |a Feng, V.W., Hirst, G., A linear-time bottom-up discourse parser with constraints and post-editing (2014) Acl, pp. 511-521 | ||
| 504 | |a Heilman, M., Sagae, K., (2015) Fast Rhetorical Structure Theory Discourse Parsing, , http://arxiv.org/abs/1505.02425 | ||
| 504 | |a Surdeanu, M., Hicks, T., Valenzuela-Escárcega, M.A., Two practical rhetorical structure theory parsers (2015) Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 1-5 | ||
| 504 | |a Hernault, H., Prendinger, H., DuVerle, D.A., Ishizuka, M., HILDA: A discourse parser using Support Vector Machine classification (2010) Dialogue & Discourse, 1 (3), pp. 1-33 | ||
| 504 | |a Liu, Y., Chawla, N.V., Harper, M.P., Shriberg, E., Stolcke, A., A study in machine learning from imbalanced data for sentence boundary detection in speech (2006) Computer Speech & Language, 20 (4), pp. 469-494 | ||
| 504 | |a Witten, I., Frank, E., Hall, M., Pal, C., The WEKA workbench. Online appendix for data mining: Practical machine learning tools and techniques (2016) Ser. The Morgan Kaufmann Series in Data Management Systems, , Fourth Edition Elsevier Science | ||
| 504 | |a Gravano, A., Benus, S., Hirschberg, J., Mitchell, S., Vovsha, I., Classification of discourse functions of affirmative words in spoken dialogue (2007) Interspeech, pp. 1613-1616 | ||
| 504 | |a Lai, C., What do you mean, you're uncertain?: The interpretation of cue words and rising intonation in dialogue (2010) Interspeech, pp. 1-4 | ||
| 504 | |a Domínguez, M., Farrús, M., Burga, A., Wanner, L., The information structureprosody language interface revisited (2014) Proceedings of the 7th International Conference on Speech Prosody (SP2014), pp. 539-543. , Dublin, IrelandA4 - Amazon Alexa; Apple; DiDi; et al.; Furhat Robotics; Microsoft | ||
| 520 | 3 | |a This work aims to explore the correlation between the discourse structure of a spoken monologue and its prosody by predicting discourse relations from different prosodic attributes. For this purpose, a corpus of semi-spontaneous monologues in English has been automatically annotated according to the Rhetorical Structure Theory, which models coherence in text via rhetorical relations. From corresponding audio files, prosodic features such as pitch, intensity, and speech rate have been extracted from different contexts of a relation. Supervised classification tasks using Support Vector Machines have been performed to find relationships between prosodic features and rhetorical relations.Preliminary results show that intensity combined with other features extracted from intra- and intersegmental environments is the feature with the highest predictability for a discourse relation. The prediction of rhetorical relations from prosodic features and their combinations is straightforwardly applicable to several tasks such as speech understanding or generation. Moreover, the knowledge of how rhetorical relations should be marked in terms of prosody will serve as a basis to improve speech synthesis applications and make voices sound more natural and expressive. Copyright © 2017 ISCA. |l eng | |
| 536 | |a Detalles de la financiación: Ministry of Economy, Trade and Industry, METI | ||
| 536 | |a Detalles de la financiación: Air Force Office of Scientific Research | ||
| 536 | |a Detalles de la financiación: 645012 | ||
| 536 | |a Detalles de la financiación: Agencia Nacional de Promoción Científica y Tecnológica, PICT 2014-1561 | ||
| 536 | |a Detalles de la financiación: U.S. Air Force | ||
| 536 | |a Detalles de la financiación: This work is part of the KRISTINA project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement number 645012. The second author is partially funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramón y Cajal program. The third and fourth authors are partially funded by ANPCYT PICT 2014-1561, and the Air Force Office of Scientific Research, Air Force Material Command, USAF under Award No. FA9550-15-1-0055. | ||
| 593 | |a TALN Research Group, DTIC, Universitat Pompeu Fabra, Barcelona, Spain | ||
| 593 | |a Departamento de Computación, FCEyN, Universidad de Buenos Aires, Argentina | ||
| 593 | |a Instituto de Investigación en Ciencias de la Computación, CONICET-UBA, Buenos Aires, Argentina | ||
| 593 | |a School of Informatics, University of Edinburgh, Edinburgh, United Kingdom | ||
| 593 | |a Catalan Institute for Research and Advanced Studies, Barcelona, Spain | ||
| 690 | 1 | 0 | |a DISCOURSE STRUCTURE |
| 690 | 1 | 0 | |a PROSODY |
| 690 | 1 | 0 | |a RST |
| 690 | 1 | 0 | |a SPEECH SYNTHESIS |
| 690 | 1 | 0 | |a SUPPORT VECTOR MACHINES |
| 690 | 1 | 0 | |a CONTINUOUS SPEECH RECOGNITION |
| 690 | 1 | 0 | |a SPEECH |
| 690 | 1 | 0 | |a SPEECH SYNTHESIS |
| 690 | 1 | 0 | |a SUPPORT VECTOR MACHINES |
| 690 | 1 | 0 | |a TEXT PROCESSING |
| 690 | 1 | 0 | |a DISCOURSE STRUCTURE |
| 690 | 1 | 0 | |a PROSODIC FEATURES |
| 690 | 1 | 0 | |a PROSODY |
| 690 | 1 | 0 | |a RHETORICAL RELATIONS |
| 690 | 1 | 0 | |a RHETORICAL STRUCTURE THEORY |
| 690 | 1 | 0 | |a SPEECH RATES |
| 690 | 1 | 0 | |a SPEECH UNDERSTANDING |
| 690 | 1 | 0 | |a SUPERVISED CLASSIFICATION |
| 690 | 1 | 0 | |a SPEECH COMMUNICATION |
| 700 | 1 | |a Farrús, M. | |
| 700 | 1 | |a Gravano, A. | |
| 700 | 1 | |a Pérez, J.M. | |
| 700 | 1 | |a Lai, C. | |
| 700 | 1 | |a Wanner, L. | |
| 700 | 1 | |a Lacerda F. | |
| 700 | 1 | |a Strombergsson S. | |
| 700 | 1 | |a Wlodarczak M. | |
| 700 | 1 | |a Heldner M. | |
| 700 | 1 | |a Gustafson J. | |
| 700 | 1 | |a House D. | |
| 700 | 1 | |a Amazon Alexa; Apple; DiDi; et al.; Furhat Robotics; Microsoft | |
| 711 | 2 | |d 20 August 2017 through 24 August 2017 |g Código de la conferencia: 132696 | |
| 773 | 0 | |d International Speech Communication Association, 2017 |g v. 2017-August |h pp. 3201-3205 |p Proc. Annu. Conf. Int. Speech. Commun. Assoc., INTERSPEECH |n Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |x 2308457X |t 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 | |
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| 856 | 4 | 0 | |u https://doi.org/10.21437/Interspeech.2017-710 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_2308457X_v2017-August_n_p3201_Kleinhans |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2308457X_v2017-August_n_p3201_Kleinhans |y Registro en la Biblioteca Digital |
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