Chronological Self-Training for Real-Time Speaker Diarization

08/05/2022

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by   Dirk Padfield, et al.

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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 each from 6 different languages and demonstrated average diarization error rates as low as 10