A new DP-like speaker clustering algorithm

In this paper we propose a new segment-synchronous speaker clustering algorithm based on the Bayesian Information Criterion (BIC), which is motivated by the Dynamic Programming (DP) idea. Compared with the commonly used agglomerative speaker clustering methods, the proposed algorithm is faster for lack of distance-matrix building and more reasonable as it avoids in some degree the simple irrevocable merging fashion. Moreover it facilitates online speaker clustering, which is important for real-time transcription applications (e.g., broadcast news, teleconferences etc.). In our experiments on 1997 Hub4 Mandarin broadcast news development data, unsupervised speaker adaptation with this DP-like clustering achieved 17.66% relative reduction in Character Error Rate (CER) from the baseline, as much as with the clustering by the true speaker identities.