Generic Modeling Applied to Speaker Count

Abstract

The problem of determining the number of speakers participating in a conversation and building their models in short conversations, within an unknown group of speakers, is addressed in this paper. The lack of information about the number of speakers and the unavailability of sufficient data present a challenging task of efficiently estimating the speaker model parameters. The proposed method uses a novel generic speaker identification (GSID) system as a guide in the model building process. The GSID system is designed performing speaker identification where the speaker associated with the test data may not be enrolled. The models in the GSID system are employed as initial speaker models, representing the persons participating in the conversation, and are subjected to a classification-adaptation procedure. The classification is performed based on the Bhattacharyya distance between the model database and the test data being analyzed. The model database of the system is designed to consist of simple and well separated models. A technique to generate such generic models is introduced. The proposed method was applied to the speaker count problem and has produced an overall accuracy of 75.3% in determining if there were 1, 2 or 3 speakers in a conversation