RECORD
Chronological Self-Training for Real-Time Speaker Diarization
Sound · Submitted 5 Aug 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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%.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Diarization partitions an audio stream into segments based on the voices of the speakers. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Real-time diarization systems that include an enrollment step should limit enrollment training samples to reduce user interaction time. | background 0.97 | 0.08 | 0.05 | asserted 0.86 |
| s2 | 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. | contribution 0.87 | 0.94 | 0.73 | asserted 1.00 |
| s3 | We studied the tradeoff between training time and classification performance and found that 1 second is sufficient to reach over 95% accuracy. | result 0.99 | 0.96 | 0.29 | asserted 1.00 |
| s4 | 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%. | result 0.96 | 0.97 | 0.24 | asserted 1.00 |
What was examined, and what was not
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The paper
Original 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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