Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India

Authors

  • Priyesha Patel  Computer Engineering, Parul University, Post Limda, Waghodia, Gujarat, India
  • Ayushi Falke  Computer Engineering, Parul University, Post Limda, Waghodia, Gujarat, India
  • Dipen Waghela  Computer Engineering, Parul University, Post Limda, Waghodia, Gujarat, India
  • Shah Vishwa  Computer Engineering, Parul University, Post Limda, Waghodia, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT2390556

Keywords:

Speech Recognition, Indian Language, Spoken Language, Pitch, Audio Feature, Machine Learning and Deep Learning

Abstract

In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Also discussed were the distinctions between various machine learning and deep learning approaches. Finally, it will point the way for future study in Indian spoken language recognition, as well as AI technology.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2024-03-11

Issue

Volume 10, Issue 2, March-April-2024

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Research Articles

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How to Cite

[1]
Priyesha Patel, Ayushi Falke, Dipen Waghela, Shah Vishwa, " Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 2, pp.63-69, March-April-2024. Available at doi : https://doi.org/10.32628/CSEIT2390556