Continuous Speech Recognition System for Kannada Language with Triphone Modelling using HTK
Anand H. Unnibhavi1, D.S.Jangamshetti2, Shridhar K.3

1Anand H. Unnibhavi, Dept. of Electronics and Communication Basaveshwara Engineering College ,Bagalkot-587103, India.
2D.S.Jangamshetti , Dept. of Electrical and Electronics Basaveshwara Engineering College, Bagalkot-587103, India.
3Shridhar K. Dept. of Electronics and Communication Basaveshwara Engineering College, Bagalkot-587103, India.

Manuscript received on 11 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 7727-7831 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5394098319/2019©BEIESP | DOI: 10.35940/ijrte.C5394.098319

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Abstract: Abstract: Kannada is the regional language of India spoken in Karnataka. This paper presents development of continuous kannada speech recognition system using monophone modelling and triphone modelling using HTK. Mel Frequency Cepstral Coefficient (MFCC) is used as feature extractor, exploits cepstral and perceptual frequency scale leads good recognition accuracy. Hidden Markov Model is used as classifier. In this paper Gaussian mixture splitting is done that captures the variations of the phones. The paper presents performance of continuous Kannada Automatic Speech Recognition (ASR) system with respect to 2, 4,8,16 and 32 Gaussian mixtures with monophone and context dependent tri-phone modelling. The experimental result shows that good recognition accuracy is achieved for context dependent tri-phone modelling than monophone modelling as the number Gaussian mixture is increased.
Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated by Commas.

Scope of the Article:
Natural Language Processing