发表机构
Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究揭示流式说话人日志系统小数据自适应提升检测却损害说话人归属，重放缓解退化但牺牲跨域迁移，强调需联合评估检测、身份一致性与保留行为。
AI 中文摘要
小数据自适应可以提高语音检测性能，同时却会降低说话人归属的准确性。我们在一个已发布的流式说话人日志系统上研究了这一差异，该系统在7.5小时的双人对话数据上进行了自适应，并在六个语料库上进行了评估。自适应显著提升了域内说话人日志性能，并能迁移至一个独立语料库。然而，这种提升在不同评估场景下并不一致，因为额外的混淆主要与时间身份一致性受损相关，而非说话人数错误。一种局部重映射诊断揭示了不同语料库间身份退化的不同模式，表明自适应可能改变流式模型随时间维持说话人分配的方式。重放（rehearsal）减少了观察到的退化，但降低了跨域迁移性能。这些结果强调了在自适应流式说话人日志系统时，需要联合评估检测准确性、身份一致性和保留行为。
英文摘要
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.
Comments5 pages, 4 figures
