International Journal of Research in Signal Processing, Computing & Communication System Design

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Assamese Connected Digit Recognition System


Affiliations
1 Department of Electronics and Communication Engineering, Gauhati University Institute of Science and Technology, Gauhati University, Guwahati, Assam, India
     

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In this work, we present the development of a connected digit recognition system in Assamese language. Assamese is an under-resourced language of North-East India that is widely spoken in the state of Assam. The text corpus used in this work, consists of a sequence 7 digits spoken in continuous manner. In order to capture the variations in phonetic context, the sequence of digits were arranged in such a way that, each digit occur in all the 7 positions. The speech corpus used in this work was collected from 11 native Assamese speakers out of which 5 were female while 6 were male. Mel Frequency Cepstral Coefficient (MFCC) features have been used as front-end features. We have explored the Subspace Gaussian Mixture Model (SGMM) based acoustic modeling approach in addition to the Gaussian Mixture Model (GMM) within the Hidden Markov Model (HMM) framework. Accuracies of 95.7% and 95.9% are achieved in GMM-HMM and SGMM-HMM systems respectively.

Keywords

Assamese Language, Digit Recognition, SGMM-HMM.
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  • Assamese Connected Digit Recognition System

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Authors

Barsha Deka
Department of Electronics and Communication Engineering, Gauhati University Institute of Science and Technology, Gauhati University, Guwahati, Assam, India
Abhishek Dey
Department of Electronics and Communication Engineering, Gauhati University Institute of Science and Technology, Gauhati University, Guwahati, Assam, India
S. R. Nirmala
Department of Electronics and Communication Engineering, Gauhati University Institute of Science and Technology, Gauhati University, Guwahati, Assam, India

Abstract


In this work, we present the development of a connected digit recognition system in Assamese language. Assamese is an under-resourced language of North-East India that is widely spoken in the state of Assam. The text corpus used in this work, consists of a sequence 7 digits spoken in continuous manner. In order to capture the variations in phonetic context, the sequence of digits were arranged in such a way that, each digit occur in all the 7 positions. The speech corpus used in this work was collected from 11 native Assamese speakers out of which 5 were female while 6 were male. Mel Frequency Cepstral Coefficient (MFCC) features have been used as front-end features. We have explored the Subspace Gaussian Mixture Model (SGMM) based acoustic modeling approach in addition to the Gaussian Mixture Model (GMM) within the Hidden Markov Model (HMM) framework. Accuracies of 95.7% and 95.9% are achieved in GMM-HMM and SGMM-HMM systems respectively.

Keywords


Assamese Language, Digit Recognition, SGMM-HMM.

References