Faster BIC segmentation using local speaker modeling

Segmentation is typically the most computationally expensive step involved in majority of speaker diarization systems. Bayesian Information Criterion (BIC) is a very widely adopted method for segmentation of audio data. While BIC returns fairly good results in terms of segmentation performance, it suffers from the problem of enormous complexity. Moreover, BIC based diarization systems encounter the worst case complexity when there is no change point in the input audio stream at all. Many audio streams contain fairly large segments separated by a very few change points. In such cases, it becomes impractical to employ BIC segmentation because of its complexity. In this paper, we have proposed a modification to the baseline BIC segmentation scheme, which makes use of local search information to reduce the overall complexity of the segmentation procedure. The results have been tested on several audio streams from broadcast news and the diarization runtime has been found to get reduced by a factor of 3.45, with a marginally better segmentation performance.