Wavelet-Based Mel-Frequency Cepstral Coefficients for Speaker Identification using Hidden Markov Models

TL;DR

This paper introduces a wavelet-based Mel-Frequency Cepstral Coefficients feature extraction method combined with Hidden Markov Models, significantly enhancing speaker identification accuracy in noisy environments.

Contribution

The paper presents a novel wavelet-based MFCC feature extraction technique that improves robustness and recognition accuracy over traditional MFCC methods in noisy conditions.

Findings

Achieved 99.3% recognition rate with clean data

Achieved 97.3% recognition rate with 20 dB S/N noise

Outperformed conventional MFCCs in noisy environments

Abstract

To improve the performance of speaker identification systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristic of the signal, the Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. Hidden Markov Models (HMMs) were used for the recognition stage as they give better recognition for the speaker's features than Dynamic Time Warping (DTW). Comparison of the proposed approach with the MFCCs conventional feature extraction method shows that the proposed method not only effectively reduces the influence of noise, but also improves recognition. A recognition rate of 99.3% was obtained using the proposed feature extraction technique compared to 98.7% using the MFCCs. When the test patterns were corrupted by additive white Gaussian noise with 20 dB S/N ratio, the recognition rate was 97.3% using the proposed method compared to 93.3% using the MFCCs.