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Realization of Hidden Markov Model for English Digit Recognition

by Ganesh S Pawar, Sunil S Morade
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 17
Year of Publication: 2014
Authors: Ganesh S Pawar, Sunil S Morade
10.5120/17278-7713

Ganesh S Pawar, Sunil S Morade . Realization of Hidden Markov Model for English Digit Recognition. International Journal of Computer Applications. 98, 17 ( July 2014), 37-40. DOI=10.5120/17278-7713

@article{ 10.5120/17278-7713,
author = { Ganesh S Pawar, Sunil S Morade },
title = { Realization of Hidden Markov Model for English Digit Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 17 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number17/17278-7713/ },
doi = { 10.5120/17278-7713 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:28.155600+05:30
%A Ganesh S Pawar
%A Sunil S Morade
%T Realization of Hidden Markov Model for English Digit Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 17
%P 37-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of the work described here is to compare the isolated English language digit speech recognition using Hidden Markov Model for speaker independent system. Two different datasets were collected of audio recordings for the said comparison of isolated digits of English language. Speakers here read numeric digits 0 to 9 i. e. ZERO to NINE. One corpus is self recorded signals and other is standard CUAVE dataset (36 speakers, each uttered 10 words). The training and testing samples are separated for speaker dependent and speaker independent systems. The system has been implemented using the HMM toolkit i. e. HTK by training HMMs of the words making the vocabulary on the training data. Different HMMs for individual digits have been initialized and trained to have well modeled structure. The trained system was tested on training data as well as test data and results shown that most of the speech samples were correctly recognized. The system was tested for speaker independent and dependent way, to check the changes in the recognition rate. Further this can be used by developers and researchers interested in speech recognition for English language not only for isolated digits but also for other words of English language. If clean database is available, further this can be generalized to recognize words of any language. Continuous speech can also be recognized using study of this system.

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Index Terms

Computer Science
Information Sciences

Keywords

CUAVE HTK HMM Isolated digits Speaker Independent

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