Gender Prediction by Speech Analysis

Abstract

Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensic analysis, the police need to detect criminal profile from any evidence such as voice from any calls and while in healthcare aspect, some vocal fold pathologies can be bias to a particular gender such as vocal fold cyst can be seen particularly in female patients and the patient will have problem with their voices. Three features are extract from the speech signal which are Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding (LPC) and Linear Prediction Coding Coefficient (LPCC). While for the classification, two classifier are used which are Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The recognition rate is higher for the combination of MFCC and LPCC compared to other features. SVM classifier had outperformed KNN classifier and obtained highest recognition rate of 97.45%. Lastly a graphical user interface system is develop that will record the voice of the speakers, pre-process the signal, extract MFCC and LPCC and classify it using SVM.