Experiments on speech tracking in audio documents using Gaussian mixture modeling

This paper deals with the tracking of speech segments in audio documents. We use a cepstral-based acoustic analysis and Gaussian mixture models for the representation of the training data. Three ways of scoring an audio document based on a frame-level likelihood calculation are proposed and compared. Our experiments are done on a database composed of television programs including news reports, advertisements, and documentaries. The best equal error rate obtained is approximately 12%