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A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition

Author

Listed:
  • Denis Ivanko

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia
    These authors contributed equally to this work.)

  • Dmitry Ryumin

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia
    These authors contributed equally to this work.)

  • Alexey Karpov

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia)

Abstract

This article provides a detailed review of recent advances in audio-visual speech recognition (AVSR) methods that have been developed over the last decade (2013–2023). Despite the recent success of audio speech recognition systems, the problem of audio-visual (AV) speech decoding remains challenging. In comparison to the previous surveys, we mainly focus on the important progress brought with the introduction of deep learning (DL) to the field and skip the description of long-known traditional “hand-crafted” methods. In addition, we also discuss the recent application of DL toward AV speech fusion and recognition. We first discuss the main AV datasets used in the literature for AVSR experiments since we consider it a data-driven machine learning (ML) task. We then consider the methodology used for visual speech recognition (VSR). Subsequently, we also consider recent AV methodology advances. We then separately discuss the evolution of the core AVSR methods, pre-processing and augmentation techniques, and modality fusion strategies. We conclude the article with a discussion on the current state of AVSR and provide our vision for future research.

Suggested Citation

  • Denis Ivanko & Dmitry Ryumin & Alexey Karpov, 2023. "A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition," Mathematics, MDPI, vol. 11(12), pages 1-30, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2665-:d:1168957
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    References listed on IDEAS

    as
    1. Siwei Zhou & Xuemei Wu & Fan Jiang & Qionghao Huang & Changqin Huang, 2023. "Emotion Recognition from Large-Scale Video Clips with Cross-Attention and Hybrid Feature Weighting Neural Networks," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    2. Ali Berkol & Talya Tümer-Sivri & Nergis Pervan-Akman & Melike Çolak & Hamit Erdem, 2023. "Visual Lip Reading Dataset in Turkish," Data, MDPI, vol. 8(1), pages 1-8, January.
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    Cited by:

    1. Irina Kipyatkova & Ildar Kagirov, 2023. "Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case," Mathematics, MDPI, vol. 11(18), pages 1-21, September.

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