Speech Recognition in Noisy Conditions Using Radon Transform and Discrete Cosine Transform from the Features Derived from Gammatone Filter Bank (GTFB)

This paper presents a new feature extraction technique based on a Gammatone Filter Bank (GTFB) for speech recognition using Radon Transform (RT) and Discrete Cosine Transform (DCT). In the proposed scheme speech specific features have been extracted by applying image processing technique to the patterns available from speech signal by applying Gammatone Filter Bank. Radon projections for twenty six orientations are captured. The acoustic characteristics of the Gammatone Filter Bank applied to the speech signal. DCT applied on Radon projections yields low dimensional feature vectors. The technique is computationally efficient and robust to session variations and insensitive to additive noise. The performance of the proposed algorithm is evaluated in presence of additive white Gaussian noise from (30dB to -5dB SNR) on Texas Instruments-46 (TI-46) speech database. The proposed algorithm improves the performance of the speech recognition system in noisy environment compared to the existing popular algorithms like Mel frequency Cepstral Coefficient (MFCC), Linear Predictive Cepstral Coefficients (LPCC), Perceptual Linear Prediction (PLP).