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DETECTION OF OVERLAPPING SPEECH IN MEETINGS USING SUPPORT VECTOR REGRESSION

A method of detecting overlapping speech in meetings is proposed in this paper. The eigenvalue distribution of the spatial correlation matrix reflects information on the relative power of sound sources. By applying Support Vector Regression to a set of input eigenvalues, the relative power of sources is estimated. Based on this, overlapping speech is then detected. The proposed method was evaluated using recorded meeting data, which showed that the detection rate increased by around 9% compared with the conventional Support Vector Machines approach.