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Automatic Speech Recognition Models, Tools, and Techniques: A Systematic Review

Automatic Speech Recognition Models, Tools, and Techniques: A Systematic Review

Puneet Mittal, Sukhwinder Sharma
Copyright: © 2023 |Pages: 23
ISBN13: 9781668460016|ISBN10: 1668460017|ISBN13 Softcover: 9781668460023|EISBN13: 9781668460030
DOI: 10.4018/978-1-6684-6001-6.ch002
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MLA

Mittal, Puneet, and Sukhwinder Sharma. "Automatic Speech Recognition Models, Tools, and Techniques: A Systematic Review." Deep Learning Research Applications for Natural Language Processing, edited by L. Ashok Kumar, et al., IGI Global, 2023, pp. 18-40. https://doi.org/10.4018/978-1-6684-6001-6.ch002

APA

Mittal, P. & Sharma, S. (2023). Automatic Speech Recognition Models, Tools, and Techniques: A Systematic Review. In L. Ashok Kumar, D. Karthika Renuka, & S. Geetha (Eds.), Deep Learning Research Applications for Natural Language Processing (pp. 18-40). IGI Global. https://doi.org/10.4018/978-1-6684-6001-6.ch002

Chicago

Mittal, Puneet, and Sukhwinder Sharma. "Automatic Speech Recognition Models, Tools, and Techniques: A Systematic Review." In Deep Learning Research Applications for Natural Language Processing, edited by L. Ashok Kumar, Dhanaraj Karthika Renuka, and S. Geetha, 18-40. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6001-6.ch002

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Abstract

Automatic speech recognition (ASR) has gained wide popularity in last decade. Various devices like mobile phones, computers, vehicles, and audio/video players are now being equipped with ASR technology. The increasing use and dependence on ASR technology leads to research enhancements and opportunities in this domain. This chapter provides a detailed review of various advancements in ASR systems development. It highlights history of speech recognition followed by detailed insight into recent advancements and industry leaders providing latest solutions. ASR framework has been discussed in detail which includes feature extraction techniques, acoustic modeling techniques, and language modeling techniques. The chapter also lists various popular data sets available and discusses generation of new data sets. This work will be helpful for the researchers who are new to this field and are exploring development of new speech recognition techniques.

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