Chronological Self-Training for Real-Time Speaker Diarization
| Autor: | Dirk Padfield, Daniel J. Liebling |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Sound (cs.SD) Computer Science - Computation and Language Computer science Speech recognition Computer Science - Sound Machine Learning (cs.LG) Speaker diarisation Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Self training Computation and Language (cs.CL) Electrical Engineering and Systems Science - Audio and Speech Processing |
| DOI: | 10.48550/arxiv.2208.03393 |
| Popis: | 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%. Comment: 5 pages, 5 figures, ICASSP 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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