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Speaker clustering using vector quantization and spectral clustering
Ist Teil von
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.4986-4989
Ort / Verlag
IEEE
Erscheinungsjahr
2010
Quelle
IEEE/IET Electronic Library
Beschreibungen/Notizen
We present a speaker clustering method for conversational speech recordings that contain short utterances from multiple speakers. The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is performed by a spectral clustering algorithm with cluster number estimation based on an eigen structure of the similarity matrix. We conducted experiments on five test sets with different utterance length distributions to compare the proposed method with the conventional approach based on a hierarchical agglomerative clustering using BIC stopping criterion. The results show that the proposed method significantly outperforms the conventional one in speaker diarization error rate and purity metrics.