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Speaker recognition is a very important research area where speech synthesis, and speech noise reduction are some of the major research areas. Speaker recognition is a new challenge for technologies. Many algorithms have been suggested and developed for feature extraction. This paper presents a feature extraction technique for speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). Further, this paper evaluates experiments conducted along each step of the MFCC process. Finally, the paper compares hamming window and rectangular window technique based on the number of filters for accurate and efficient result in a Matlab environment. The result indicates that using a 32 filter with hamming window has more accuracy and efficiency compared to other windowing techniques and number of filters.
Bhattarai et al. (Sat,) studied this question.