Unknown-Multiple Speaker clustering using HMM
An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown a priori. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum duration topology. The final number of clusters is determined automatically by merging closest clusters and retraining this new cluster, until a decrease in likelihood is observed. In the same framework, we also examine the effect of using only the features from highly voiced frames as a means of improving the robustness and computational complexity of the algorithm. The proposed system is assessed on the 1996 HUB-4 evaluation test set in terms of both cluster and speaker purity. It is shown that the number of clusters found often correspond to the actual number of speakers.
- URL: http://publications.idiap.ch/downloads/reports/2002/ajmera2002icslp.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/ajmera-rr-02-07
Record created on 2006-03-10, modified on 2016-08-08