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EMO-AVSR: Two-Level Approach for Audio-Visual Emotional Speech Recognition

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Speech and Computer (SPECOM 2023)

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Abstract

Emotional speech recognition is a challenging task for modern systems. The presence of emotions significantly changes the characteristics of speech. In this paper, we propose a novel approach for emotional speech recognition (EMO-AVSR). The proposed approach uses visual speech data to detect a person’s emotion first, followed by processing of speech by one of the pre-trained emotional audio-visual speech recognition models. We implement these models as a combination of spatio-temporal network for emotion recognition and a cross-modal attention fusion for automatic audio-visual speech recognition. We present experimental investigation that shows how different emotions (happy, anger, disgust, fear, sad, and neutral), valence (positive, neutral, and negative) and binary (emotional and neutral) affect automatic audio-visual speech recognition. The evaluation on CREMA-D data demonstrates up to 7.3% absolute accuracy improvement compared to the classical approaches.

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Notes

  1. 1.

    https://github.com/SMIL-SPCRAS/EMO-AVSR.

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Acknowledgments

This research is financially supported by the Russian Science Foundation (project No. 22-11-00321).

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Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russia

    Denis Ivanko, Elena Ryumina, Dmitry Ryumin, Alexandr Axyonov, Alexey Kashevnik & Alexey Karpov

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  1. Denis Ivanko
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  2. Elena Ryumina
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  3. Dmitry Ryumin
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  4. Alexandr Axyonov
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  5. Alexey Kashevnik
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Correspondence to Denis Ivanko .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    K. Samudravijaya

  3. Indian Institute of Information Technology Dharwad, Dharwad, India

    K. T. Deepak

  4. Indian Institute of Technology Dharwad, Dharwad, India

    Rajesh M. Hegde

  5. KIIT Group of Colleges, Gurugram, India

    Shyam S. Agrawal

  6. Indian Institute of Technology Dharwad, Dharwad, India

    S. R. Mahadeva Prasanna

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Ivanko, D., Ryumina, E., Ryumin, D., Axyonov, A., Kashevnik, A., Karpov, A. (2023). EMO-AVSR: Two-Level Approach for Audio-Visual Emotional Speech Recognition. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_2

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