SCDiar：基于说话人变化检测与语音识别的流式分离系统
SCDiar: a streaming diarization system based on speaker change detection and speech recognition
AI总结：
针对长会议实时语音流中说话人分离易出错的问题，提出SCDiar系统，通过SCD模块在token级别切分语音片段并优化片段选择，在超十人真实会议数据上准确率提升53.6%，显著缩小了在线与离线系统性能差距。
摘要：
在长达数小时的会议场景中，实时语音流通常难以实现准确的说话人分离，往往导致说话人识别和说话人计数错误。为了应对这一挑战，我们提出了SCDiar，该系统在语音片段上运行，这些片段由说话人变化检测（SCD）模块在token级别进行切分。基于这些片段，我们引入了几项改进，以高效地为每个说话人选择最佳可用片段。这些改进在多个基准测试中带来了显著的性能提升。值得注意的是，在涉及超过十名参与者的真实会议数据上，SCDiar的准确率比以前的系统高出53.6%，大幅缩小了在线系统与离线系统之间的性能差距。
英文摘要：
In hours-long meeting scenarios, real-time speech stream often struggles with achieving accurate speaker diarization, commonly leading to speaker identification and speaker count errors. To address this challenge, we propose SCDiar, a system that operates on speech segments, split at the token level by a speaker change detection (SCD) module. Building on these segments, we introduce several enhancements to efficiently select the best available segment for each speaker. These improvements lead to significant gains across various benchmarks. Notably, on real-world meeting data involving more than ten participants, SCDiar outperforms previous systems by up to 53.6\% in accuracy, substantially narrowing the performance gap between online and offline systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
