Identification of Speaker from Disguised Voice Using MFCC Feature Extraction, Chi-Square and Classification Technique

• Published in: Wireless personal communications Vol. 138; no. 2; pp. 973 - 987
• Main Author: Singh, Mahesh K.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-09-2024; Springer Nature B.V
• Subjects: Background noise; Chi-square test; Classification; Communications Engineering; Computer Communication Networks; Correlation coefficients; Engineering; Feature extraction; Feature recognition; Networks; Signal distribution; Signal,Image and Speech Processing; Speech recognition; Support vector machines; Voice recognition; k-NN; Chi-square; SVM; Voice disguised; MFCC
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-024-11542-0

The purpose of this manuscript is to show that certain acoustic features can be used to recognize the disguised speech of unknown speakers. As the name implies, forensic speaker identification entails the use of scientific techniques to ascertain an unknown speaker’s identity during an inquiry. This study aims to provide a voice recognition method that works well. To distinguish between speech and background noise in each frame, chi-square tests are utilized. The estimated background noise is continuously modified to achieve this. Chi-square noise estimations are then obtained once background noise has initially been reduced. The observed signal distribution and the estimated noise distribution are compared using a second chi-square test, this time using a different approach. For the frame to be labelled as noise, the chi-square test scores must be close together. Mel-frequency cepstrum coefficient (MFCC), features are grouped as three-dimensional features. The correlation coefficient characteristics of speech are coupled with the different MFCC feature extraction technique. The feature-based classification is done with support vector machine (SVM) classifiers and k-nearest neighbor (k-NN) classification technique. Classification results show that applying these unique features in an SVM classifier boosts classification accuracy.