A comparative study of different segmentation approaches for audio track indexing

This paper investigate methods for test-independent speaker-based segmentation of audio track. The focus is on online segmentation of the data. We compare the different criteria based on distance measures and linear discriminant analysis (LDA) for automatic segmentation of audio track. Experiments were performed on a TV panel show using various sets of input features. This series includes speech, music and speech of multiple speakers talking simultaneously. The experimental results show that Mahalonabis distance criterion gives better performance than other distance measures and LDA