Implementation of Machine Learning-Aided Speech Analysis for Speaker Accent Identification Applied to Audio Forensics

Abstract

Accent recognition as a subset of speech recognition is crucial in audio forensics as it provides the authenticity of the speech that can be presented to the judicature as evidence. The challenge here is to design a speaker accent identifying system that can provide significant information to match with the standards of the court of justice. This process requires robust descriptors to represent speech signal with good discrimination ability. This chapter proposes to use Mel Frequency Cepstral Coefficients to identify and represent the human utterances by transforming the frequency from normal scale to Mel scale. The work utilized support vector machine, k nearest neighbors, XG boost, linear discriminant analysis, quadratic discriminant analysis, and decision tree algorithms to recognize the accent of the speaker. The experiment conducted on the accent recognition dataset demonstrated the ability of Mel Frequency Cepstral Coefficients and kNN classifier in identifying and discriminating six accents belonging to different speakers with better accuracy.