Improvement of speech enhancement techniques for robust speaker identification in noise

This paper presents an approach of speech enhancement techniques to improve the performance of the robust speaker identification under noisy environments. Start-end points detection, silence part removal, frame segmentation and windowing technique have been used to pre-process and Wiener filter has been used to remove the silence parts from the speech utterances. To extract the features from the speech various speech parameterization techniques that is LPC, LPCC, RCC, MFCC, ¿MFCC and ¿¿MFCC have been simulated. Finally, to measure the performance of the proposed speech enhancement techniques, genetic algorithm has been used as a classifier for the noise robust automated speaker identification system and various experiments have performed on genetic algorithm to select the optimum parameters. According to the NOIZEOUS speech database, the highest identification rate of 70.31 [%] for text-dependent and of 61.26 [%] for text-independent speaker identification system have been achieved.