A text-independent speaker verification model: A comparative analysis

TL;DR

This paper compares various feature extraction, dimensionality reduction, and classification techniques in text-independent speaker verification to identify the most effective combination for accurate voice recognition.

Contribution

It provides a comprehensive comparative analysis of different methods across all stages of speaker recognition using standard corpora.

Findings

MFCC with SVM yields high accuracy

PCA and tSNE effectively reduce dimensionality

Decision tree classifier performs well in certain configurations

Abstract

The most pressing challenge in the field of voice biometrics is selecting the most efficient technique of speaker recognition. Every individual's voice is peculiar, factors like physical differences in vocal organs, accent and pronunciation contributes to the problem's complexity. In this paper, we explore the various methods available in each block in the process of speaker recognition with the objective to identify best of techniques that could be used to get precise results. We study the results on text independent corpora. We use MFCC (Melfrequency cepstral coefficient), LPCC (linear predictive cepstral coefficient) and PLP (perceptual linear prediction) algorithms for feature extraction, PCA (Principal Component Analysis) and tSNE for dimensionality reduction and SVM (Support Vector Machine), feed forward, nearest neighbor and decision tree algorithms for classification block in speaker recognition system and comparatively analyze each block to determine the best technique