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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17068

Title: Improving Valence Prediction in Dimensional Speech Emotion Recognition Using Linguistic Information
Authors: Atmaja, Bagus Tris
Akagi, Masato
Keywords: valence prediction
linguistic feature
speech emotion recognition
dimensional emotion
affective computing
Issue Date: 2020-11-06
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)
Start page: 166
End page: 171
DOI: 10.1109/O-COCOSDA50338.2020.9295032
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.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA), 2020, pp.166-171. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/17068
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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