Online speaker clustering

The paper describes a set of new algorithms that perform speaker clustering in an online fashion. Unlike typical clustering approaches, the proposed method does not require the presence of all the data before performing clustering. The clustering decision is made as soon as an audio segment is received. Being causal, this method enables low-latency incremental speaker adaptation in online speech-to-text systems. It also gives a speaker tracking and indexing system the ability to label speakers with cluster ID on the fly. We show that the new online speaker clustering method yields better performance compared to the traditional hierarchical speaker clustering. Evaluation metrics for speaker clustering are also discussed.