Automatic Speech and Speaker Recognition by MFCC,HMM & Vector Quantization
Journal: International journal of research In Computer engineering and Electronics (Vol.2, No. 4)Publication Date: 2013-08-01
Authors : Deshmukh S.D Prof.Bachute;
Page : 1-6
Keywords : Hidden Markov Model (HMM); Mel Frequency Cepstral Coefficients (MFCC); Vector Quantization; Euclidian Distance; Viterbi algorithm; forword & backword algorithm;
Abstract
The speech and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human machine interfaces which are most useful for handicap person to live the better life. The speaker recognition process relies heavily on frequency analysis. This can be done because each person has some very unique characteristics to their voice that can be isolated in the frequency domain. This paper presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency for the improvement of speech feature representation in a HMM based recognition approach. There are two strong reasons why Hidden Morkov Model is used. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications
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Last modified: 2013-09-11 19:33:37