Comparative Analysis of Automatic Speech Recognition Techniques

DOI

10.2991/978-94-6463-136-4_79How to use a DOI?

Keywords

ASR; MFCC; DTW; HMM; ML

Abstract

It’s most crucial method of transferring data is interaction. Speech is the most common way of data exchange. According to with linguistic survey, there are 179 languages and 544 dialects spoken in India. Current India has 18 scheduled dialects and several unscheduled languages. The primary goal of this paper is to give a thorough comparative evaluation of the relevant research on automated speech recognition. We observe potential prospects, problems, and methodologies, as well as locate, evaluate, and synthesize data from research in order to give empirical responses to scientific concerns. The survey was done by using appropriate research publications period between 2010 and 2021. The goal of this comprehensive examination is to synthesize the current best research on automated speech recognition by combining the findings of several investigations.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Suvarnsing G. Bhable
AU  - Ratnadeep R. Deshmukh
AU  - Charansing N. Kayte
PY  - 2023
DA  - 2023/05/01
TI  - Comparative Analysis of Automatic Speech Recognition Techniques
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 897
EP  - 904
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_79
DO  - 10.2991/978-94-6463-136-4_79
ID  - Bhable2023
ER  -