A Deep Learning Approach for Bangla Speech to Text Conversion

One of the most remarkable advantages of speech recognition (SR) technology includes speech to text conversion (STT). This paper's focal point is to recognize Bangla words among various speakers and noisy environments and convert them into text. Mel frequency cepstral coefficient (MFCC) has been used for extracting words from the audio file. Using gated recurrent units (GRU) along with directional long short term memory (LSTM), the continuity of words and next word-level prediction in a sentence have been implemented. An existing dataset that is enriched by recorded speech has been used here. Keras-Tensorflow toolkit has been used as a software apparatus for training data. The highest train accuracy has been obtained from the GRU architecture in the acoustic model (AM) is 94.44% while the test accuracy rate is only 47% due to the small test dataset. On the other hand, test accuracy is maximum up to 45.81% with the LSTM model in the language model (LM). The proposed method signifies good training and testing accuracy rate based on applying GRU model to the AM, LSTM to the LM and the numbers of total recordings of each sentence.