arXiv cs.CL· Mo Yu, Yang Liu, Jing Qian·· 10 天前AI 评分20
流式说话人分离器适配中的指标遮蔽：遗忘为何看似提升，以及 rehearsal 的代价
When Forgetting Looks Like Improvement: Metric Masking in Streaming Diarizer Adaptation and the Price of Rehearsal
AI 导读
一项 arXiv 研究在 7.5 小时双人对话数据上适配一个已发布的流式说话人分离器，并在六个语料库上评测，发现适配显著提升域内分离性能且可迁移到独立语料，但改进在不同评测场景下并不一致，额外混淆主要来自时间身份一致性受损而非说话人数错误。局部重映射诊断显示不同语料的身份退化模式各异，rehearsal 虽能减轻退化却会降低跨域迁移性能。
正文 · 原文
Abstract:Small-data adaptation can improve speech detection while degrading speaker attribution. We study this discrepancy in a released streaming diarizer adapted on 7.5 h of two-party conversation and evaluated across six corpora. Adaptation substantially improves in-domain diarization performance and transfers to an independent corpus. However, this improvement is not consistent across evaluation scenarios as the additional confusion is mainly associated with impaired temporal identity consistency rather than speaker-count errors. A local-remapping diagnostic reveals different patterns of identity degradation across corpora, indicating that adaptation may alter how streaming models maintain speaker assignments over time. Rehearsal reduces the observed degradation but reduces the cross-domain transfer performance. These results highlight the need to jointly evaluate detection accuracy, identity consistency, and retention behavior when adapting streaming diarization systems.
| Comments: | 5 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2610.08828 [cs.CL] |
| (or arXiv:2610.08828v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08828 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mo Yu [view email]
[v1]
Sat, 26 Sep 2026 04:07:01 UTC (458 KB)
来源：arXiv cs.CL · arxiv.org