Modular Arithmetic and Wavelets for Speaker Verification

The aim of this study is to concentrate on optimizing dimensionality of feature space by selecting the number of repeating the remainder (modular arithmetic) applied for a speech signal with Wavelet Packet (WP) upon level three features extraction method. The functions of features extraction and classification were performed using the modular arithmetic, wavelet packet and three verification functions (MWVS) expert system. This was accomplished by decreasing the number of feature vector elements of individual speaker obtained by using modular arithmetic and wavelet packet method (MWM) ( 285 elements). To investigate the performance of the proposed MWVS method, two other verification methods were proposed: Gaussian mixture model based method (GMMW) and K-Means clustering based method (KMM). The results indicated that a better verification rate for the text-independent system was accomplished by MWVS and GMMW. Better result was achieved (91.36%) in case of the speaker-speaker verification system. In case of white Gaussian noise (AWGN), it was observed that the MWVS system is generally more noise-robust in case of using approximate discrete wavelet transform sub-signal instead of the original signal. The system works in real time. This was performed by the recording apparatus and a data acquisition system (NI-6024E) and interfacing online with Matlab code that simulates the expert system. A major contribution of this study is the development of a less computational complexity speaker verification system with modular arithmetic capable of dealing with abnormal conditions for relatively good degree.