Improved speaker segmentation and segments clustering using the bayesian information criterion
Detection of speaker, channel and environment changes in a continuous audio stream is important in various applications (e.g., broadcast news, meetings/teleconferences etc.). Standard schemes for segmentation use a classi er and hence do not generalize to unseen speaker / channel / environments. Recently S.Chen introduced new segmentation and clustering algorithms, using the so-called BIC. This paper presents more accurate and more e cient variants of the BIC scheme for segmentation and clustering. Speci cally, the new algorithms improve the speed and accuracy of segmentation and clustering and allow for a real-time implementation of simultaneous transcription, segmentation and speaker tracking.