Skip to Main Content
The main objective of this paper is to explore the effectiveness of feature selection for performing composite speaker identification/verification. We propose features such as line spectral frequency (LSF), differential line spectral frequency (DLSF), mel frequency cepstral coefficients (MFCC), discrete cosine transform cepstrum (DCTC), perceptual linear predictive cepstrum (PLP) and mel frequency perceptual linear predictive cepstrum (MF-PLP). These features are captured and training models are developed by K-means clustering procedure. A speaker identification system is evaluated on noise added test speeches and the experimental results reveal the performance of the proposed algorithm in identifying speakers based on minimum distance between test features and clusters and also highlight the best choice of feature set among all the proposed features for 50 speakers chosen randomly from "TIMIT" database. Analysis is performed on the identification results to emphasize the choice of features which produce better results for speaker verification with respect to equal error rate. In this work, F-ratio is computed as a theoretical measure to validate the experimental results for both identification and verification.