Cluster criterion functions in spectral subspace and their application in speaker clustering

Authors:
Trung Hieu Nguyen; Haizhou Li;Eng Siong Chng
Affiliations:
Institute for Infocomm Research, Department of Human Language Technology, 1 Fusionopolis Way, #21-01 Connexis, South Tower, Singapore 138632;Institute for Infocomm Research, Department of Human Language Technology, 1 Fusionopolis Way, #21-01 Connexis, South Tower, Singapore 138632;Nanyang Technological University, School of Computer Engineering, Block N4, Nanyang Avenue, Singapore 639798
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

In this paper, we propose two cluster criterion functions which aim to maximize the separation between intra-cluster distances and inter-cluster distances. These criteria can automatically deduce the desired number of clusters based on their extremized values. We then propose an algorithm to apply our criterion functions in conjunction with spectral clustering. By exploiting the characteristic of spectral subspace, we show that the speakers are more separable in this subspace which will further enhance the effectiveness of our proposed criteria. The algorithm is used in our agglomerative hierarchical speaker diarization system to test on Rich Transcription 2007 conference data set and obtains very good results.