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DBT: multimodal emotion recognition based on dual-branch transformer

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Abstract

There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling experts to identify emotion categories, while the wav2vec2.0 model is a general model for obtaining speech representations through self-supervised training. Therefore, we try to apply it to speech-emotion recognition tasks. We propose a multimodal dual-branch transformer network. For the speech processing branch, first, we use wav2vec2.0 to extract speech features. Then, a fine-tuning strategy and a self-attention-based interlayer feature fusion strategy are used. Second, a fully convolutional classification network is used for emotion classification. Then, we use RoBERTa for text emotion recognition and bimodal fusion by an improved weighted Dempster–Shafer (DS) strategy. In addition, we propose an accuracy-weighted label smoothing method, which can improve recognition accuracy. We perform comprehensive experiments on two benchmarks: IEMOCAP and CASIA, covering both Chinese and English datasets. The experimental results show that the proposed method has higher accuracy than state-of-the-art methods.

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Data Availability Statement

Data cannot be made available.

Notes

  1. http://www.chineseldc.org/resource_info.php?rid=76.

  2. https://huggingface.co/facebook/wav2vec2-large-960h-lv60.

  3. https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn.

  4. https://huggingface.co/roberta-large?text=The+goal+of+life+is+%3Cmask%3E.

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Funding

This study was supported by the National Key R &D Program of China under Grant 2020YFC0833102.

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Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yufan Yi, Yan Tian, Cong He, Yajing Fan & Yiping Xu

  2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Xinli Hu

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  1. Yufan Yi
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Contributions

YY, CH, YT, and YX contributed to the conception of the study. YY, CH, and YF performed the experiment. YY, YT and CH, YX contributed significantly to the analysis and manuscript preparation. YY and CH performed the data analyses and wrote the manuscript. YT, YX, and XH helped perform the analysis with constructive discussions. All authors reviewed the manuscript.

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Correspondence to Yan Tian.

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Yi, Y., Tian, Y., He, C. et al. DBT: multimodal emotion recognition based on dual-branch transformer. J Supercomput 79, 8611–8633 (2023). https://doi.org/10.1007/s11227-022-05001-5

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