Robust Support Vector Machines for Speaker Verification Task

TL;DR

This paper enhances speaker verification accuracy by combining spectral features and applying PCA for noise robustness, demonstrating significant improvements in noisy conditions using SVM classifiers.

Contribution

It introduces a robust feature combination and PCA-based dimension reduction method to improve SVM-based speaker verification performance in noisy environments.

Findings

Improved verification accuracy with combined spectral features.

PCA reduces dimensionality effectively in noisy conditions.

Significant performance gains in low SNR environments.

Abstract

An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conventional Mel-cepstral Coefficients (MFCCs) and Line Spectral Frequencies (LSFs) to constitute robust multivariate feature vectors. To reduce the high dimensionality required for training these feature vectors, we use a dimension reduction method called principal component analysis (PCA). In order to evaluate the robustness of these systems, different noisy environments have been used. The obtained results using TIMIT database showed that, using the paradigm that combines these spectral cues leads to a significant improvement in verification accuracy, especially with PCA reduction for low signal-to-noise ratio noisy environment.