Improved architecture of speaker recognition based on wavelet transform and mel frequency cepstral coefficient (MFCC)

Combining mel frequency cepstral coefficient with wavelet transform for feature extraction is not new. this paper proposes a new architecture to help in increasing the accuracy of speaker recognition compared with conventional architecture. in conventional speaker model,the voice will undergo noise elimination first before feature extraction. the proposed architecture however,will extract the features and eliminate noise simultaneously. the mfcc is used to extract the voice features while wavelet de-noising technique is used to eliminate the noise contained in the speech signals. thus,the new architecture achieves two outcomes in one single process: ex-tracting voice feature and elimination of noise. © 2017 universiti putra malaysia press.