| Universal Background Models for Real-Time Speaker Change Detection (2008) | |||||||||||||||
Abstract | |||||||||||||||
| This paper addresses the problem of real-time speaker change detection in TV news broadcast, in which no prior knowledge on speakers is assumed. To enhance the effect of the reliable frames in a speech stream, we propose a new approach to feature categorization based on Gaussian Mixture Model- Universal Background Model (GMM-UBM). The feature vectors are categorized into three sets, which include reliable speech, doubtful speech and unreliable speech. Then a novel distance measure is presented for real-time speaker change detection. Extensive experiments demonstrate the superior performance of the proposed approach. The intrinsic difficulties on real-time speaker change detection are discussed as well in this paper. 1 | |||||||||||||||
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