Bangla Spoken Numerals Recognition by Using HMM

Abstract

Speech is one of the most natural forms of vocalized communication media. Nowadays with the advancement of machine learning, different doors are opened to us for finding several standard ways to step out in the real world. ASR is just like the door to explore the concept of communication through speech between human and digital devices that can recognize speech. In this paper, we have designed a Hidden Markov Model-based isolated Bangla numerals recognition system where the Short-Term Fourier Transform is used for collecting the feature vectors. The defined system achieved 91.50% accuracy for our own dataset of 2000 uttered samples for 10 classes, which gives a satisfied result for this Bangla numerals recognition. IntroductionStandard communication is always done successfully through speech. Speech helps us to complete a smooth communication among us. Those who are not computer professionals can communicate through speech because of its easiness and coziness in communication purposes. As the people of India live in a semi-illiterate country, so with the help of the application which supports speech recognition they will be more benifitable and can take the advantage of modern science. Speech recognition is the way that helps people to communicate with computer through speech. In speech recognition, isolated words are recognized and after converting it to text format, finally, it is prepared to a machine-readable format. Speech recognition performs a major role in medical unit, home automation, for growing the development of realworld market-based applications, which is used basically for commercial purposes. The application of speech recognition will become more useful in that field where the keyboard operation is not suitable. Speech is really needful to the users who mainly use hands and eyes for their work, like mail-sorter, aircraft pilot, cartographer, etc. as