Speech Emotion Recognition with Co-Attention Based Multi-Level Acoustic Information

Heqing Zou · Yuke Si · Chen Chen · Deepu Rajan · Eng Siong Chng

184 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Speech Emotion Recognition (SER) aims to help the machine to understand human’s subjective emotion from only audio in-formation. 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, BiL-STM and wav2vec2, respectively. Then these extracted features are treated as multimodal inputs and fused by the pro-posed 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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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2001Emotion recognition in human-computer interaction 20183-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition 2015High-level feature representation using recurrent neural network for speech emotion recognition 2018An Attention Pooling Based Representation Learning Method for Speech Emotion Recognition

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2023Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features 2024Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition 2023SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing 2023Speech emotion recognition approaches: A systematic review 2025Speech Emotion Recognition via CNN-Transformer and multidimensional attention mechanism

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