Development and Evaluation of Automatic -Speaker based- Audio Identification and Segmentation for Broadcast News Recordings Indexation

In this paper, we describe an automatic- speaker based- audio segmentation and identification system for broadcasted news indexation purposes. We specifically focus on speaker identification and audio scene detection. Speaker identification (SI) is based on the state of the art Gaussian mixture models, whereas scene change detection process uses the classical Bayesian Information Criteria (BIC) and the recently proposed DISTBIC algorithm. In this work, the effectiveness of Mel Frequency Cepstral coefficients MFCC, Linear Predictive Cepstral Coefficients LPCC, and Log Area Ratio LAR coefficients are compared for the purpose of text-independent speaker identification and speaker based audio segmentation. Both the Fisher Discrimination Ratio-feature analysis and performance evaluation in terms of correct identification rate on the TIMIT database showed that the LPCC outperforms the other features especially for low order coefficients. Our experiments on audio segmentation module showed that the DISTBIC segmentation technique is more accurate than the BIC procedure especially in the presence of short segments.