Emotion Recognition with Speech and Facial Images

XUE Peiyun; DAI Shutao; BAI Jing; GAO Xiang

doi:10.11999/JEIT240087

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XUE Peiyun, DAI Shutao, BAI Jing, GAO Xiang. Emotion Recognition with Speech and Facial Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240087

Citation:

XUE Peiyun, DAI Shutao, BAI Jing, GAO Xiang. Emotion Recognition with Speech and Facial Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240087

XUE Peiyun, DAI Shutao, BAI Jing, GAO Xiang. Emotion Recognition with Speech and Facial Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240087

Citation:

XUE Peiyun, DAI Shutao, BAI Jing, GAO Xiang. Emotion Recognition with Speech and Facial Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240087

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Emotion Recognition with Speech and Facial Images

doi: 10.11999/JEIT240087

XUE Peiyun^{1, 2
,
,},
DAI Shutao¹,
BAI Jing¹,
GAO Xiang¹

1.
Taiyuan University of Technology, College of Electronic Information and Optical Engineering, Taiyuan 030024, China
2.
Shanxi Advanced Innovation Research Institute, Postdoctoral orkstation, Taiyuan 030032, China

Funds: Shanxi Youth Fundation (20210302124544), Applied Basic Research Program of Shanxi Province (201901D111094)

Received Date: 2024-02-05
Rev Recd Date: 2024-11-06

Available Online: 2024-11-08

Abstract

Abstract

In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
- Emotion recognition,
- Attention mechanism,
- Multi-branch convolution,
- Residual mixing,
- Decision fusion

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References(37)

References

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