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.
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Paul, B., Adhikary, D., Dey, T., Guchhait, S., Bera, S. (2022). Bangla Spoken Numerals Recognition by Using HMM. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_8
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DOI: https://doi.org/10.1007/978-981-16-2543-5_8
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