A Comparative Study of Phoneme Recognition using GMM-HMM and ANN based Acoustic Modeling | Zendy

Farheen Fauziya | Zendy; Geeta Nijhawan | Zendy

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A Comparative Study of Phoneme Recognition using GMM-HMM and ANN based Acoustic Modeling

Author(s) -

Farheen Fauziya,

Geeta Nijhawan

Publication year - 2014

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/17186-7366

Subject(s) - computer science , hidden markov model , speech recognition , pattern recognition (psychology) , artificial intelligence

Phoneme is the smallest analogous unit of sound employed to form meaningful contrast between utterances. Hidden Markov Model (HMM), Gaussian Mixture model (GMM) and Artificial Neural Network (ANN) have been used in this paper to measure the accuracy and performance of recognition system using toolkits HTK, Sphinx3 and Quicknet, which are freely available for academic works. In this paper the performance of an ASR System based on Accuracy has been compared with TIMIT database.

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