Speech is the standard vocalized communication media. Speech is one of the comfortable way for humans to communicate with each other. Similarly, speech recognition system is eagerly necessary to communicate with computer through voice. Speech recognition in English language already helps us to operate English voice command-based applications. But in rural and semi-urban areas, due to lack of knowledge in English in India, it is necessary to implement automatic speech recognition in regional languages. Here, we have built a Gaussian Mixture Model (GMM)-based Bengali (also called Bangla) isolated spoken numerals recognition system where mel frequency cepstral coefficients denoted as MFCC is taken for feature extraction. The proposed system achieved 91.7% correct prediction for the Bangla numeral data set of 1000 audio samples for 10 classes which is satisfactory for previous Bangla spoken digit recognition.
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