Digital Signal Processing

Yogesh S. Angal ¹, Amol D. Rahulkar ², Raghunath S. Holambe ³, Rajan H. Chile ³

Affiliations
1 Department of Instrumentation Engineering, Padmashree Dr. D.Y. Patil Institute of Engineering and Technology, Pimpri, Pune-18, Maharashtra State (M.S.), IN
2 S.G.G.S. Institute of Engineering and Technology, Nanded, IN
3 Department of Instrumentation Engineering, S.G.G.S. Institute of Engineering and Technology, Nanded-431607, Maharashtra State (M.S.), IN

Abstract

This paper presents a new technique to extract the speech features in order to improve the recognition accuracy in various types of noisy environments. Most of the speech recognition systems are suffered from high computational complexity. In this paper, a new class of triplet half band wavelet packets (THWP) has been designed based on the generalized half band polynomial. These packets are used in speech recognition system to derive the effective and efficient speech features. The proposed THWP satisfies perfect reconstruction (PR) and provides linear phase, regularity, better frequency-selectivity and near orthogonality. These properties are exploited to approximate desirable speech features significantly. The proposed technique computes features using energy, mean and variance of each sub-band of THWP. This gives low dimensional feature vectors for speech recognition purpose. The performance of the proposed algorithm has been evaluated on Texas Instruments-46 (TI-46) speech database in various noisy environments. The performance of the proposed technique is better than existing popular speech recognition algorithms.

Keywords

Filter Bank, Half Band Filters, Feature Extraction, Wavelet Transform, THWP, Speech Recognition.

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