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arXiv · 2208.03393

Chronological Self-Training for Real-Time Speaker Diarization

Dirk Padfield·Daniel J. Liebling
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Abstract

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%.

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BibTeXRIS

Dirk Padfield, Daniel J. Liebling. 2022-08-05. Chronological Self-Training for Real-Time Speaker Diarization. https://doi.org/10.21437/interspeech.2021-822

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