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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17069

Title: Predicting Valence and Arousal by Aggregating Acoustic Features for Acoustic-Linguistic Information Fusion
Authors: Atmaja, Bagus Tris
Hamada, Yasuhiro
Akagi, Masato
Keywords: valence, arousal
affective computing
feature aggregation
feature fusion
Issue Date: 2020-11-19
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 IEEE REGION 10 CONFERENCE (TENCON)
Start page: 1081
End page: 1085
DOI: 10.1109/TENCON50793.2020.9293899
Abstract: This paper presents an evaluation of acoustic feature aggregation and acoustic-linguistic features combination for valence and arousal prediction within a speech. First, acoustic features were aggregated from chunk-based processing for story-based processing. We evaluated mean and maximum aggregation methods for those acoustic features and compared the results with the baseline, which used majority voting aggregation. Second, the extracted acoustic features are combined with linguistic features for predicting valence and arousal categories: low, medium, or high. The unimodal result using acoustic features aggregation showed an improvement over the baseline majority voting on development partition for the same acoustic feature set. The bimodal results (by combining acoustic and linguistic information at the feature level) improved both development and test scores over the official baseline. This combination of acoustic-linguistic information targeted speech-based applications where acoustic and linguistic features can be extracted from the sole speech modality.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 IEEE REGION 10 CONFERENCE (TENCON), 2020, pp.1081-1085. 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/10119/17069
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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