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Clustering And Segmenting Speakers And Their Locations In Meetings

This paper presents a new approach toward automatic annotation of meetings in terms of speaker identities and their locations. This is achieved by segmenting the audio recordings using two independent sources of information : magnitude spectrum analysis and sound source localization. We combine the two in an appropriate HMM framework. There are three main advantages of this approach. First, it is completely unsupervised, i.e. speaker identities and number of speakers and locations are automatically inferred. Second, it is threshold-free, i.e. the decisions are made without the need of a threshold value which generally requires an additional development dataset. The third advantage is that the joint segmentation improves over the speaker segmentation derived using only acoustic features. Experiments on a series of meetings recorded in the IDIAP Smart Meeting Room demonstrate the effectiveness of this approach.

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