Clustering Target Speaker on a Set of Telephone Dialogs

The ability of the speaker’s voice model to reproduce detailed parameterization of individual speech features is an important property for its use in solving different biometric problems. In general case one of the main reasons of performance degradation in voice biometric systems is the voice variability that occurs when speaker’s state (emotional, physiological, etc.) or channel conditions are changing. Therefore, accurate modeling of the intra-speaker voice variability leads to a more accurate voice model. This can be achieved by collecting multiple speech samples of the same speaker recorded in diverse conditions to create so-called multi-session model. We consider the case when speech data is represented by dialogues recorded in a single channel. This setup raises the problem of grouping the segments of a target speaker from the set of dialogues. We propose a clustering algorithm to solve this problem, which is based on the probabilistic linear discriminant analysis (PLDA). Our experiments demonstrate effectiveness of the proposed approach compared to solutions based on exhaustive search.