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Automatic speech recognition: a survey

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Abstract

Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.

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Authors and Affiliations

  1. Punjab University College of Information Technology (PUCIT), Lahore, Pakistan

    Mishaim Malik

  2. Faculty of Punjab University College of Information Technology (PUCIT), Lahore, Pakistan

    Muhammad Kamran Malik

  3. School of Engineering and Information Technology, University of New South Wales (UNSW) Canberra at ADFA, Canberra, Australia

    Khawar Mehmood

  4. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

    Imran Makhdoom

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Malik, M., Malik, M.K., Mehmood, K. et al. Automatic speech recognition: a survey. Multimed Tools Appl 80, 9411–9457 (2021). https://doi.org/10.1007/s11042-020-10073-7

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