Abstract
An array of devices have emerged lately for easing our daily life but one concern has always been towards designing simple user interface (UI) for such devices. A speech-based UI can be a solution to this, considering the fact that it is one of the most spontaneous and natural modes of interaction for most people. The process of identification of words and phrases from voice signals is known as Speech Recognition. Every language encompasses a unique set of atomic sounds termed as Phonemes. It is these sounds which constitute the vocabulary of that language. Speech Recognition in Bangla is a bit complicated task mostly due to the presence of compound characters. In this paper, a Bangla Phoneme Recognition system is proposed to help in the development of a Bangla Speech Recognizer using a new Linear Predictive Cepstral Coefficient-based feature, namely LPCC-2. The system has been tested on a data set of 3710 Bangla Swarabarna (Vowel) Phonemes, and an accuracy of 99.06% has been obtained using Ensemble Learning.
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The authors would like to thank the students of West Bengal State University for voluntarily providing the voice samples during data collection.
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Mukherjee, H., Phadikar, S., Roy, K. (2018). An Ensemble Learning-Based Bangla Phoneme Recognition System Using LPCC-2 Features. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_7
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