Indexing telephone conversations by speakers using time-frequency principal component analysis

In this paper, we present an algorithm for the tracking of target speakers in telephone conversations. Speaker tracking consists in retrieving, in an audio recording, segments which have been uttered by a target speaker. We also compare two speech analysis techniques. The first one is the time-frequency principal component analysis. It is a new speech analysis technique based on the extraction of the principal components of the contextual covariance matrix, which is the covariance matrix of feature vectors expanded by their time context. The other one is the classical cepstral analysis. Experiments are carried out on a subset of the Switchboard database.