Abstract
This paper investigates methods for text-independent, speaker-based segmentation of audio track. The focus is on on-line 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 are 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.