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

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

ICASSP 2022accepted0 citations

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

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

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.

BibTeX

@inproceedings{icassp2022_speechemotionrec,
  title = {Speech Emotion Recognition with Co-Attention Based Multi-Level Acoustic Information},
  author = {Heqing Zou and Yuke Si and Chen Chen and Deepu Rajan and Eng Siong Chng},
  booktitle = {ICASSP 2022},
  year = {2022}
}