Speech Emotion Recognition with Multi-task Learning

被引：40

作者：

Cai, Xingyu ^{[1
]}

Yuan, Jiahong ^{[1
]}

Zheng, Renjie ^{[1
]}

Huang, Liang ^{[1
]}

Church, Kenneth ^{[1
]}

机构：

[1] Baidu Res, Sunnyvale, CA 94089 USA

来源：

INTERSPEECH 2021 | 2021年

关键词：

speech emotion recognition; multi-task learning; MODELS;

D O I：

10.21437/Interspeech.2021-1852

中图分类号：

R36 [病理学]; R76 [耳鼻咽喉科学];

学科分类号：

100104 ; 100213 ;

摘要：

Speech emotion recognition (SER) classifies speech into emotion categories such as: Happy, Angry, Sad and Neutral. Recently, deep learning has been applied to the SER task. This paper proposes a multi-task learning (MTL) framework to simultaneously perform speech-to-text recognition and emotion classification, with an end-to-end deep neural model based on wav2vec-2.0. Experiments on the IEMOCAP benchmark show that the proposed method achieves the state-of-the-art performance on the SER task. In addition, an ablation study establishes the effectiveness of the proposed MTL framework.

引用

页码：4508 / 4512

页数：5

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