LAUSR

A phoneme classification model has been developed for Bengali continuous speech in this experiment. The analysis was conducted using a deep neural network based classification model. In the first phase, phoneme classification task has been performed using the deep-structured classification model along with two baseline models. The deep-structured model provided better overall classification accuracy than the baseline systems which were designed using hidden Markov model and multilayer Perceptron respectively. The confusion matrix of all the Bengali phonemes generated by the classification model is observed, and the phonemes are divided into nine groups. These nine groups provided better overall classification accuracy of 98.7%. In the next phase of this study, the place and manner of articulation based phonological features are detected and classified. The phonemes are regrouped into 15 groups using the manner of articulation based knowledge, and the deep-structured model is retrained. The system provided 98.9% of overall classification accuracy this time. This is almost equal to the overall classification accuracy which was observed for nine phoneme groups. But as the nine phoneme groups are redivided into 15 groups, the phoneme confusion in a single group became less which leads to a better phoneme classification model.