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Mispronunciation detection and diagnosis using deep neural networks: a systematic review

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Abstract

The increased need for foreign language learning, along with advances in speech technology have heightened interest in computer-assisted pronunciation teaching (CAPT) applications. Herein, the automatic diagnosis of pronunciation errors is essential, it allows language learners to identify their mispronunciations and thus improve their oral skills. Meanwhile, the emergence of deep learning algorithms for speech processing led to the use of deep neural networks at several stages of the mispronunciation detection and diagnosis process. Therefore, an overview of the state-of-the-art in deep learning algorithms for mispronunciation diagnosis is needed, for which we performed a systematic literature review. This study aims to provide an overview of the recent use of deep neural networks for mispronunciation detection and diagnosis (MDD). A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 53 papers published between the years 2015 and 2023. This review indicates that the diagnosis of pronunciation errors is a highly active area of research. Quite a few deep learning models and approaches have been proposed in this area, but there are still some important open issues and limitations to be addressed in future works.

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  1. Computer Science Department, University Badji Mokhtar, Annaba, Algeria

    Meriem Lounis, Bilal Dendani & Halima Bahi

  2. LISCO Laboratory, University Badji Mokhtar, Annaba, Algeria

    Meriem Lounis, Bilal Dendani & Halima Bahi

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Lounis, M., Dendani, B. & Bahi, H. Mispronunciation detection and diagnosis using deep neural networks: a systematic review. Multimed Tools Appl 83, 62793–62827 (2024). https://doi.org/10.1007/s11042-023-17899-x

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