Deep neural network-based phoneme classification of standard Khasi dialect in continuous speech. (26th December 2019)

• Record Type: Journal Article
• Authors: Syiem, Bronson; Singh, L. Joyprakash
• Is Part Of: International journal of applied pattern recognition. Volume 6:Number 1(2019)
• Page Start: 43
• Page End: 51
• Publication Date: 2019-12-26
• Subjects: acoustic model -- deep neural network -- DNN -- Gaussian mixture model -- GMM -- hidden Markov model -- HMM -- language model -- Mel frequency cepstral coefficient -- MFCC -- phone error rate -- PER -- voice activity detection -- VAD -- word error rate -- WER; Pattern perception -- Periodicals; Pattern recognition systems -- Periodicals; 006.405
• Languages: English
• ISSNs: 2049-887X

Abstract

In this paper, deep neural network (DNN) is used to classify phonemes of the standard Khasi dialect which is one of the commonly used dialects in the state of Meghalaya. For this, clean speech data were recorded in the laboratory from native speakers. In the proposed system, a monophone and a triphone hidden Markov models (HMMs) were also built to compare the results obtained. Our finding showed that DNN outperformed the classification over the other two models with a classification accuracy of 89.70%.