Speaker clustering using vector representation with long-term feature for lecture speech recognition

Speaker clustering has been widely adopted for clustering the speech data based on acoustic characteristics so that an unsupervised speaker normalization and speaker adaptive training can be applied for a better speech recognition performance. In this study, we present a vector space speaker clustering approach with long-term feature analysis. The supervector based on the GMM mean vectors is adopted to represent the characteristics of speakers. To achieve a robust representation, total variability subspace modeling, which has been successfully applied in speaker recognition for compensating channel and session variability over the GMM mean supervector, is used for speaker clustering. We apply a long-term feature analysis strategy to average short-time spectral features over a period of time to capture the speaker traits that are manifested over a speech segment longer than a spectral frame. Experiments conducted on lecture style speech show that this speaker clustering approach offers a better speech recognition performance.