Automatic speech recognition: a survey

• Published in: Multimedia tools and applications Vol. 80; no. 6; pp. 9411 - 9457
• Main Authors: Malik, Mishaim, Malik, Muhammad Kamran, Mehmood, Khawar, Makhdoom, Imran
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Automatic speech recognition; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Feature extraction; Language modeling; Machine learning; Multimedia Information Systems; Special Purpose and Application-Based Systems; Speech recognition; Voice recognition; ASR; Feature extraction; Classification models; Language models; Speech recognition; Automatic speech recognition
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10073-7

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.