Speaker identification system using PCA & eigenface

This paper presents a speech-based speaker identification system and an efficient approach for selection of acoustic parameters closely related to the vocal track shape of the speaker. Speech endpoint detection algorithm is developed in order to discard the room noise and non-speech signal to achieve high accuracy of the system. Windowing and fast Fourier transform (FFT) are used to determine the spectrum of the speech signal and PCA has been used to extract feature of speech of individual speaker. Eigenface algorithm has been used here as a classification and recognition tool. Eigenspace of individual speaker is generated by the feature of the speech signal. The experimental results show the noticeable performance of the proposed system.