pith:QN4MLT5L
Exploring Speech Foundation Models for Speaker Diarization Across Lifespan
Speech diarization models trained only on adults lose accuracy on child and older-adult conversations but recover with joint multi-age training.
arxiv:2604.05201 v2 · 2026-04-06 · eess.AS
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Claims
Results show substantial performance degradation when models trained on adult-specific speech are applied to child and older-adult conversational data. Moreover, joint multi-age training across different age groups improves robustness without reducing diarization performance in canonical adult conversations, while targeted age group adaptation yields further gains in diarization performance, particularly when using the Whisper encoder.
That observed performance differences are caused primarily by age-related acoustic domain shift rather than differences in recording quality, conversation style, or speaker count across the chosen datasets.
Adult-trained speech foundation models lose diarization accuracy on child and older-adult speech, but multi-age joint training and adaptation improve robustness across the lifespan.
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| First computed | 2026-05-20T01:05:12.526245Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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# expect: 8378c5cfab946f3cea3380ee4be050b010bcd4dc3e35a91ec4ec0149c3ca0279
Canonical record JSON
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