Hybrid wavelet based LPC features for Hindi speech recognition

Authors:
Aditya Sharma;M. C. Shrotriya;Omar Farooq;Z. A. Abbasi
Affiliations:
Department of Electronics Engineering, AMU, Aligarh, India.;Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand – 835215, India.;Department of Electronics Engineering, AMU, Aligarh, India.;Department of Electronics Engineering, AMU, Aligarh, India
Venue:
International Journal of Information and Communication Technology
Year:
2008

Citing 7
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Abstract

Hybrid features are presented for speech recognition that uses linear prediction in combination with multi-resolution capabilities of wavelet transform. Wavelet-Based Linear Prediction Coefficients (WBLPC) are obtained by applying 3 and 4-level wavelet decomposition and then having linear prediction of each sub-bands to get total 13 features. These features have been tested using a linear discriminant function and Hidden Markov Model (HMM) based classifier for speaker dependent and independent isolated Hindi digits recognition. 3-level WBLPC features gave higher percentage recognition than LPC features while 4-level WBLPC features using HMM gave the highest percentage recognition for both speaker dependent and independent cases.