Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information

• URL: http://arxiv.org/abs/2203.15326v1
• Date: Tue, 29 Mar 2022 08:17:28 GMT
• Title: Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information
• Authors: Heqing Zou, Yuke Si, Chen Chen, Deepu Rajan, Eng Siong Chng
• Abstract summary: Speech Emotion Recognition aims to help the machine to understand human's subjective emotion from only audio information. We propose an end-to-end speech emotion recognition system using multi-level acoustic information with a newly designed co-attention module.
• Score: 21.527784717450885
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Speech Emotion Recognition (SER) aims to help the machine to understand human's subjective emotion from only audio information. However, extracting and utilizing comprehensive in-depth audio information is still a challenging task. In this paper, we propose an end-to-end speech emotion recognition system using multi-level acoustic information with a newly designed co-attention module. We firstly extract multi-level acoustic information, including MFCC, spectrogram, and the embedded high-level acoustic information with CNN, BiLSTM and wav2vec2, respectively. Then these extracted features are treated as multimodal inputs and fused by the proposed co-attention mechanism. Experiments are carried on the IEMOCAP dataset, and our model achieves competitive performance with two different speaker-independent cross-validation strategies. Our code is available on GitHub.

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