Automatic speech recognition: a survey

Mishaim Malik

doi:10.1007/s11042-020-10073-7

Automatic speech recognition: a survey

2020-11-10

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Mishaim Malik *

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Muhammad Kamran Malik

DOI：10.1007/s11042-020-10073-7

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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.

Keyword:

Speech recognition

ASR

Automatic speech recognition

Feature extraction

Classification models

Language models

Automatic speech recognition: a survey

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