Unknown-Multiple Speaker clustering using HMM
| Type of publication: | Conference paper |
| Citation: | ajmera2002icslp |
| Booktitle: | ICSLP |
| Year: | 2002 |
| Address: | Denver, Colorado |
| Note: | IDIAP-RR 02-07 |
| Crossref: | ajmera-rr-02-07: |
| Abstract: | An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown \emph{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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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