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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16657

Title: Speech Emotion Recognition Using Speech Feature and Word Embedding
Authors: Atmaja, Bagus Tris
Shirai, Kiyoaki
Akagi, Masato
Issue Date: 2019-11-19
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Start page: 519
End page: 523
DOI: 10.1109/APSIPAASC47483.2019.9023098
Abstract: Emotion recognition can be performed automatically from many modalities. This paper presents a categorical speech emotion recognition using speech feature and word embedding. Text features can be combined with speech features to improve emotion recognition accuracy, and both features can be obtained from speech. Here, we use speech segments, by removing silences in an utterance, where the acoustic feature is extracted for speech-based emotion recognition. Word embedding is used as an input feature for text emotion recognition and a combination of both features is proposed for performance improvement purpose. Two unidirectional LSTM layers are used for text and fully connected layers are applied for acoustic emotion recognition. Both networks then are merged by fully connected networks in early fusion way to produce one of four predicted emotion categories. The result shows the combination of speech and text achieve higher accuracy i.e. 75.49% compared to speech only with 58.29% or text only emotion recognition with 68.01%. This result also outperforms the previously proposed methods by others using the same dataset on the same modalities.
Rights: This is the author's version of the work. Copyright (C) 2019 IEEE. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019, pp.519-523. 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/10119/16657
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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