UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 11 | Issue 10 | October 2024

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Volume 10 Issue 11
November-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2311407


Registration ID:
528241

Page Number

e44-e48

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Title

A Comparative Study of AI Techniques for Speech-to-Text Conversion

Abstract

Speech-to-text conversion, also known as automatic speech recognition (ASR), has become increasingly important in many applications, including virtual assistants, captioning, and transcription. However, building accurate and robust ASR systems is challenging due to the variability of speech signals, including background noise, speaker accents, and speech styles. In recent years, transfer learning has emerged as a promising technique for improving the performance of deep learning models in various tasks, including natural language processing. In this paper, we investigate the effectiveness of transfer learning in the context of speech-to-text systems. the potential of transfer learning in improving the accuracy and robustness of speech-to-text systems. This approach has practical implications for building ASR systems in various domains and scenarios.

Key Words

Speech-to-text, automatic speech recognition, transfer learning, fine-tuning, feature extraction, deep learning.

Cite This Article

"A Comparative Study of AI Techniques for Speech-to-Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e44-e48, November-2023, Available :http://www.jetir.org/papers/JETIR2311407.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Comparative Study of AI Techniques for Speech-to-Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe44-e48, November-2023, Available at : http://www.jetir.org/papers/JETIR2311407.pdf

Publication Details

Published Paper ID: JETIR2311407
Registration ID: 528241
Published In: Volume 10 | Issue 11 | Year November-2023
DOI (Digital Object Identifier):
Page No: e44-e48
Country: Mohali, Punjab, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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