Deep Neural Network Based Recognition and Classification of Bengali Phonemes: A Case Study of Bengali Unconstrained Speech

This paper proposed a phoneme recognition and classification model for Bengali continuous speech. A Deep Neural Network based model has been developed for the recognition and classification task where the Stacked Denoising Autoencoder is used to generatively pre-train the deep network. Autoencoders are stacked to form the deep-structured network. Mel-frequency cepstral coefficients are used as input data vector. In hidden layer, 200 numbers of hidden units have been utilized. The number of hidden layers of the deep network is kept as three. The phoneme posterior probability has been derived in the output layer. This proposed model has been trained and tested using unconstrained Bengali continuous speech data collected from the different sources (TV, Radio, and normal conversation in a laboratory). In recognition phase, the Phoneme Error Rate is reported for the deep-structured model as 24.62% and 26.37% respectively for the training and testing while in the classification task this model achieves 86.7% average phoneme classification accuracy in training and 82.53% in the testing phase.