Abstract
In this generation of IT, communicating with machines in an expedient manner using human speech that too in their own language is highly desirable. This is achieved using speech recognition systems which allow the general public to speak to the machine by recognizing their voice. Hindi being the most widely spoken language with approx. 260 million first-language speakers [1] should have a real-time recognition system. The main objective of this paper is to develop a speaker-independent system which can recognize continuous Hindi speech in real-time scenario. This paper presents the feasibility of MFCC for feature extraction and dynamic time warping to compare the test sequence. The system has been trained on 8 h of audio data and a trigram language model trained with 30K words. With a dictionary of 6K words, the system gives a word accuracy of 80–85%.
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Acknowledgements
The assistance of Mr. A.K. Dhamija in this work is acknowledged. We also thank the management of KIIT College of Engineering, above all Dr. Harsh Vardhan Kamrah and Dr. Neelima Kamrah for providing required facilities, financial help, and support.
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Sharan, S., Bansal, S., Agrawal, S.S. (2018). Speaker-Independent Recognition System for Continuous Hindi Speech Using Probabilistic Model. In: Agrawal, S., Devi, A., Wason, R., Bansal, P. (eds) Speech and Language Processing for Human-Machine Communications. Advances in Intelligent Systems and Computing, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-6626-9_10
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DOI: https://doi.org/10.1007/978-981-10-6626-9_10
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