Speech recognition is the most interactive technology between a human and a machine. Over the past 70 years, tremendous work has been accomplished in this fundamental area of speech communication. However, implementation of the Bengali language is unsubstantial in the field of Human-Computer Interaction. This research paper tried to implement convolution neural network technique for creating a speech recognition system in the Bengali language. We also implemented recurrent neural network to find the Bengali character probabilities which were then improved further by using CTC loss function and language model. This paper implemented Bengali language consisting of diacritic characters and such languages are very much difficult to train in a model.
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A Speech Recognition System for Bengali Language using Recurrent Neural Network
Semantic Scholar · Computer Science · 2019
Abstract
Speech recognition is the most interactive technology between a human and a machine. Over the past 70 years, tremendous work has been accomplished in this fundamental area of speech communication. However, implementation of the Bengali language is unsubstantial in the field of Human-Computer Interaction. This research paper tried to implement convolution neural network technique for creating a speech recognition system in the Bengali language. We also implemented recurrent neural network to find the Bengali character probabilities which were then improved further by using CTC loss function and language model. This paper implemented Bengali language consisting of diacritic characters and such languages are very much difficult to train in a model.