Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion

In this paper, we are interested in detecting changes in speaker identity, environmental condition and channel condition; we call this the problem of acoustic change detection. The input audio stream can be modeled as a Gaussian process in the cepstral space. We present a maximum likelihood approach to detect turns of a Gaussian process; the decision of a turn is based on the Bayesian Information Criterion (BIC), a model selection criterion well-known in the statistics literature. The BIC criterion can also be applied as a termination criterion in hierarchical methods for clustering of audio segments: two nodes can be merged only if the merging increases the BIC value. Our experiments on the Hub4 1996 and 1997 evaluation data show that our segmentation algorithm can successfully detect acoustic changes; our clustering algorithm can produce clusters with high purity, leading to improvements in accuracy through unsupervised adaptation as much as the ideal clustering by the true speaker identities.