LibriheavyMix：一个用于单通道混响多说话人语音分离、自动语音识别和说话人日志的20,000小时数据集
LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
AI总结：
本文发布LibriheavyMix大规模远场重叠语音数据集，并构建分离-识别-日志流水线基准，以解决单通道混响多说话人场景下“谁在何时说了什么”的难题。
摘要：
语音处理领域的发展日益聚焦于复杂场景，例如具有多个同时说话人和远场条件的会议或鸡尾酒会。现有应对这些挑战的方法分为两类：多通道和单通道解决方案。单通道方法以其通用性和便利性著称，不需要关于麦克风阵列的具体信息。本文提出了一个大规模远场重叠语音数据集，旨在推进语音分离、识别和说话人日志的研究。该数据集是解码多说话人混响环境中“谁在何时说了什么”这一领域内艰巨挑战的关键资源。此外，我们引入了一个包含语音分离、识别和日志的流水线系统，作为基础基准。在WHAMR!数据集上的评估验证了所提出数据的广泛适用性。
英文摘要：
The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions. Existing methodologies for addressing these challenges fall into two categories: multi-channel and single-channel solutions. Single-channel approaches, notable for their generality and convenience, do not require specific information about microphone arrays. This paper presents a large-scale far-field overlapping speech dataset, crafted to advance research in speech separation, recognition, and speaker diarization. This dataset is a critical resource for decoding ``Who said What and When'' in multi-talker, reverberant environments, a daunting challenge in the field. Additionally, we introduce a pipeline system encompassing speech separation, recognition, and diarization as a foundational benchmark. Evaluations on the WHAMR! dataset validate the broad applicability of the proposed data.
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。
