Automatic Speaker Recognition using LPCC and MFCC

Abstract

A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. This report talks about speaker recognition which deals with the subject of identifying a person based on their unique voiceprint present in their speech data. Pre-processing of the speech signal is performed before voice feature extraction. This process ensures the voice feature extraction contains accurate information that conveys the identity of the speaker. Voice feature extraction methods such as Linear Predictive Coding (LPC), Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) are analysed and evaluated for their suitability for use in speaker recognition tasks. A new method which combined LPCC and MFCC (LPCC+MFCC) using fusion output was proposed and evaluated together with the different voice feature extraction methods. The speaker model for all the methods was computed using Vector Quantization- Linde, Buzo and Gray (VQ-LBG) method. Individual modelling and comparison for LPCC and MFCC is used for the LPCC+MFCC method. The similarity scores for both methods are then combined for identification decision. The results show that this method is better or at least comparable to the traditional methods such as LPCC and MFCC.
DOI: 10.17762/ijritcc2321-8169.160474