HMM based isolated Kannada digit recognition system using MFCC

Author

Muralikrishna, H. ; Ananthakrishna, T. ; Shama, Kumara

Author_Institution

Dept. of E&C, MIT, Manipal, India

fYear

2013

Firstpage

730

Lastpage

733

Abstract

In this paper we have implemented Kannada isolated digit recognition system using Mel frequency cepstral coefficients (MFCC) as feature vector. The system is designed to recognize isolated utterances of Kannada numbers. MFCC are used as the features and Hidden Markov Model (HMM) as pattern recognizer. K-means procedure is performed on the feature vectors to obtain the observation sequence. Discrete HMM is used in the system. The system is developed by considering the requirement of a voice controlled machine in Kannada language. Performance of the system is evaluated and compared based on the MFCC along with its first and second order derivatives.

Keywords

hidden Markov models; natural language processing; speech recognition; HMM based isolated Kannada digit recognition system; K-means procedure; Kannada language; Mel frequency cepstral coefficients; discrete HMM; feature vector; first order derivatives; hidden Markov model; isolated utterances recognition; observation sequence; pattern recognition; second order derivatives; voice controlled machine; Data models; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech processing; Speech recognition; Hidden Markov Model (HMM); Kannada language; Mel frequency cepstral coefficients (MFCC); speech recognition; vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on

Conference_Location

Mysore

Print_ISBN

978-1-4799-2432-5

Type

conf

DOI

10.1109/ICACCI.2013.6637264

Filename

6637264