Comparative wavelet, PLP, and LPC speech recognition techniques on the Hindi speech digits database
Annapurna Mishra, M. C. Shrotriya, Shivendra Nath Sharan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
In view of the growing use of automatic speech recognition in the modern society, we study various alternative representations of the speech signal that have the potential to contribute to the improvement of the recognition performance. In this paper wavelet based features using different wavelets are used for Hindi digits recognition. The recognition performance of these features has been compared with Linear Prediction Coefficients (LPC) and Perceptual Linear Prediction (PLP) features. All features have been tested using Hidden Markov Model (HMM) based classifier for speaker independent Hindi digits recognition. The recognition performance of PLP features is11.3% better than LPC features. The recognition performance with db10 features has shown a further improvement of 12.55% over PLP features. The recognition performance with db10 is best among all wavelet based features.