用先进模型提升医疗场景下的语音分说话人性能，效果显著。
TCG CREST System Description for the DISPLACE-M Challenge
- 采用Diarizen端到端系统与SpeechBrain流水线对比，结合多种聚类方法。
- 在开发集和评测集上分别实现10.37%和9.21%的错误率，较基线提升39%。
- 适合关注语音识别在复杂医疗场景中应用的研究者或开发者。
本文介绍TCG CREST系统在DISPLACE-M挑战赛第一赛道（说话人分隔）中的设计，聚焦于嘈杂农村医疗环境中的自然对话。研究评估了不同语音活动检测（VAD）方法及先进聚类算法对整体说话人分隔（SD）性能的影响。对比两种框架：基于SpeechBrain与ECAPA-TDNN嵌入的模块化流水线，以及基于预训练WavLM的SOTA端到端系统Diarizen。在此基础上，探索多种聚类技术，包括层次聚类（AHC）和多种新型谱聚类变体，如SC-adapt、SC-PNA和SC-MK。实验表明，相较于SpeechBrain基线，Diarizen系统在第一阶段后评估中实现约39%的相对错误率（DER）改进。最佳提交系统以Diarizen为基础，结合采用更大上下文窗口（29）的中值滤波的AHC，开发集上达到10.37%的DER，评测集为9.21%。团队在11支参赛队伍中排名第5。
原文摘要 · Abstract (English)
This report presents the TCG CREST system description for Track 1 (Speaker Diarization) of the DISPLACE-M challenge, focusing on naturalistic medical conversations in noisy rural-healthcare scenarios. Our study evaluates the impact of various voice activity detection (VAD) methods and advanced clustering algorithms on overall speaker diarization (SD) performance. We compare and analyze two SD frameworks: a modular pipeline utilizing SpeechBrain with ECAPA-TDNN embeddings, and a state-of-the-art (SOTA) hybrid end-to-end neural diarization system, Diarizen, built on top of a pre-trained WavLM. With these frameworks, we explore diverse clustering techniques, including agglomerative hierarchical clustering (AHC), and multiple novel variants of spectral clustering, such as SC-adapt, SC-PNA, and SC-MK. Experimental results demonstrate that the Diarizen system provides an approximate $39\%$ relative improvement in the diarization error rate (DER) on the post-evaluation analysis of Phase~I compared to the SpeechBrain baseline. Our best-performing submitted system employing the Diarizen baseline with AHC employing a median filtering with a larger context window of $29$ achieved a DER of 10.37\% on the development and 9.21\% on the evaluation sets, respectively. Our team ranked fifth out of the 11 participating teams after the Phase~I evaluation.
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