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Adaptive HMM based Speech Recognition to Recognize Multi-lingual Sentence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 7 |
| Year of Publication: 2015 |
| Authors: Shrurti Gupta, Kashif Shabeeb, Sonika Singh, Sandeep Sharma |
10.5120/20165-2271
|
Shrurti Gupta, Kashif Shabeeb, Sonika Singh, Sandeep Sharma . Adaptive HMM based Speech Recognition to Recognize Multi-lingual Sentence. International Journal of Computer Applications. 115, 7 ( April 2015), 28-32. DOI=10.5120/20165-2271
Abstract
Hidden Markov Models (HMMs) provides an effective framework for the modeling of time-varying sequence of spectral vector. An approach of mapping signal to discrete signal is to define it as a set of acoustic featured symbol over a minimal but constant time interval. The aim of proposing this paper is to recognize the speech sample using hidden markov model (HMM) with the use of cepstrum feature of our given speech sample within an adaptive interval of time for which pitch period is determined and divides the sample in accordance with this period. Secondly the phonetics or exact "pronunciation" of the word needs to be defined. These are established by associated rule probability where probability is done on word's pronunciation.
References
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