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Proceedings Paper

Comparative wavelet, PLP, and LPC speech recognition techniques on the Hindi speech digits database
Author(s): A. N. Mishra; M. C. Shrotriya; S. N. Sharan
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Paper Abstract

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.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754634 (26 February 2010); doi: 10.1117/12.856318
Show Author Affiliations
A. N. Mishra, Birla Institute of Technology (India)
M. C. Shrotriya, Birla Institute of Technology (India)
S. N. Sharan, Greater Noida Institute of Technology (India)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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