  Diarization partitions an audio stream into segments based on the voices of
the speakers. Real-time diarization systems that include an enrollment step
should limit enrollment training samples to reduce user interaction time.
Although training on a small number of samples yields poor performance, we show
that the accuracy can be improved dramatically using a chronological
self-training approach. We studied the tradeoff between training time and
classification performance and found that 1 second is sufficient to reach over
95% accuracy. We evaluated on 700 audio conversation files of about 10 minutes
each from 6 different languages and demonstrated average diarization error
rates as low as 10%.
