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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