Performance evaluation of Hindi speech recognition system using optimized filterbanks

An Automatic Speech Recognition (ASR) system implementation uses a conventional pattern recognition technique that stores a set of training patterns in classes and compares the test patterns with training patterns to place them in the best matched pattern class. Most state-of-the-art ASR systems use Mel Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP) to extract features in training phase of the ASR system. However, sensitivity of MFCC & PLP to background noise has resulted in use of noise robust features Gammatone Frequency Cepstral Coefficient (GFCC) and Basilar-membrane Frequency-band Cepstral Coefficient (BFCC). But many issues associated with these feature extraction methods, like accepted bandwidth and standard number of filters are unresolved till date. This paper proposes a novel approach to use Differential Evolution (DE) algorithm to optimize the number and spacing of filters used in MFCC, GFCC and BFCC techniques. It also evaluates the performance of the said feature extraction methods with and without DE optimization in clean as well as in noisy environments. The results conclude that BFCC based ASR systems performs 0.4% to 1.0% better than GFCC and 7% to 10% better than MFCC in different conditions.