Speaker Recognition -- Wavelet Packet Based Multiresolution Feature Extraction Approach

TL;DR

This paper introduces a hybrid wavelet packet and MFCC feature extraction method for speaker recognition, demonstrating improved accuracy and noise robustness on multiple speech datasets.

Contribution

It presents a novel hybrid feature extraction approach combining MFCC and wavelet packet transform for enhanced speaker recognition performance.

Findings

Improved speaker identification accuracy.

Enhanced noise robustness in speaker verification.

Effective performance on multiple speech corpora.

Abstract

This paper proposes a novel Wavelet Packet based feature extraction approach for the task of text independent speaker recognition. The features are extracted by using the combination of Mel Frequency Cepstral Coefficient (MFCC) and Wavelet Packet Transform (WPT).Hybrid Features technique uses the advantage of human ear simulation offered by MFCC combining it with multi-resolution property and noise robustness of WPT. To check the validity of the proposed approach for the text independent speaker identification and verification we have used the Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) respectively as the classifiers. The proposed paradigm is tested on voxforge speech corpus and CSTR US KED Timit database. The paradigm is also evaluated after adding standard noise signal at different level of SNRs for evaluating the noise robustness. Experimental results show that better results are achieved for the tasks of both speaker identification as well as speaker verification.