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A Survey Report on Speech Recognition System

by Moirangthem Tiken Singh, Abdur Razzaq Fayjie, Biswajeet Kachari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 11
Year of Publication: 2015
Authors: Moirangthem Tiken Singh, Abdur Razzaq Fayjie, Biswajeet Kachari
10.5120/21581-4672

Moirangthem Tiken Singh, Abdur Razzaq Fayjie, Biswajeet Kachari . A Survey Report on Speech Recognition System. International Journal of Computer Applications. 121, 11 ( July 2015), 1-3. DOI=10.5120/21581-4672

@article{ 10.5120/21581-4672,
author = { Moirangthem Tiken Singh, Abdur Razzaq Fayjie, Biswajeet Kachari },
title = { A Survey Report on Speech Recognition System },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 11 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number11/21581-4672/ },
doi = { 10.5120/21581-4672 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:08.449342+05:30
%A Moirangthem Tiken Singh
%A Abdur Razzaq Fayjie
%A Biswajeet Kachari
%T A Survey Report on Speech Recognition System
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 11
%P 1-3
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech Recognition is the process of converting an acoustic waveform into text containing the similar information conveyed by speaker. This paper present a report on a Automatic Speech Recognition System (ASR) for different language under different accent. The paper describe the methods used and comparative study of the performance of every system so far developed. The study shows that Hidden Markov Model(HMM) as classifier and Mel Frequency Cepstral Coefficients(MFCC) as speech features are the most common technique used. And Moreover ASR implemented by using Hidden Markov Tool kit(HTK) are more efficient then the other systems implemented by using other tools

References
  1. Mohit Dua et al. , "Punjabi Automatic Speech Recognition Using HTK. " in IJCSI International Journal of Computer Science Issues, IJCSI press, Mauritius, Vol. 1, Issue 4, No. 1, Jul. 2012.
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  5. Ganesh S. Pawar, Sunil S. Morade, "Isolated English Language Digit Recognition Using Hidden Markov Model Tool kit. " in International Journal of Advanced Research in Computer Science and Software Engineering, Jaunpur-222001, Uttar Pradesh, India, Vol. 4, Issue 6, June 2014.
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  8. Babita Saxena and Charu Wahi, "Hindi Digits Recognition System On Speech Data Collected in Natural Noise Environments. " in David C. Wyld et al. (Eds) : CSITY, SIGPRO, DTMN - 2015.
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Index Terms

Computer Science
Information Sciences

Keywords

Hidden Markov Model (HMM) MFCC Different Language Accent Hidden Markov Tool kit(HTK)

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