Improving Valence Prediction in Dimensional Speech Emotion Recognition Using Linguistic Information

Bagus Tris Atmaja, Masato Akagi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

In dimensional emotion recognition, a model called valence, arousal, and dominance is widely used. The current research in dimensional speech emotion recognition has shown a problem that the performance of valence prediction is lower than arousal and dominance. This paper presents an approach to tackle this problem: Improving the low score of valence prediction by utilizing linguistic information. Our approach fuses acoustic features with linguistic features, which is a conversion from words to vectors. The results doubled the performance of valence prediction on both single-task learning single-output (predicting valence only) and multitask learning multi-output (predicting valence, arousal, and dominance). Using a proper combination of acoustic and linguistic features not only improved valence prediction, but also improved arousal and dominance predictions in multitask learning.

Original languageEnglish
Title of host publicationProceedings of 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages166-171
Number of pages6
ISBN (Electronic)9781728198965
DOIs
Publication statusPublished - 5 Nov 2020
Event23rd Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2020 - Virtual, Yangon, Myanmar
Duration: 5 Nov 20207 Nov 2020

Publication series

NameProceedings of 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2020

Conference

Conference23rd Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2020
Country/TerritoryMyanmar
CityVirtual, Yangon
Period5/11/207/11/20

Keywords

  • affective computing
  • dimensional emotion
  • linguistic feature
  • speech emotion recognition
  • valence prediction

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