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A domain-agnostic approach for opinion prediction on speech

Santos, Pedro Bispo ; Beinborn, Lisa ; Gurevych, Iryna
Hrsg.: Nissim, Malvina ; Patti, Viviana ; Plank, Barbara (2016)
A domain-agnostic approach for opinion prediction on speech.
Osaka, Japan
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state- of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2016
Herausgeber: Nissim, Malvina ; Patti, Viviana ; Plank, Barbara
Autor(en): Santos, Pedro Bispo ; Beinborn, Lisa ; Gurevych, Iryna
Art des Eintrags: Bibliographie
Titel: A domain-agnostic approach for opinion prediction on speech
Sprache: Englisch
Publikationsjahr: Dezember 2016
Verlag: The COLING 2016 Organizing Committee
Buchtitel: Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
Veranstaltungsort: Osaka, Japan
URL / URN: http://www.aclweb.org/anthology/W16-4318
Zugehörige Links:
Kurzbeschreibung (Abstract):

We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state- of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.

Freie Schlagworte: reviewed;UKP_reviewed;UKP_p_AudioVisual
ID-Nummer: TUD-CS-2016-14666
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen
Hinterlegungsdatum: 31 Dez 2016 14:29
Letzte Änderung: 24 Jan 2020 12:03
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