基于吸引子的多话语语音分离，针对未知说话人数
Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers
AI总结：
本文提出一种基于吸引子的单通道语音分离模型，能同时分离、估计未知说话人数并检测活动，在多话语场景下优于现有方法。
摘要：
本文解决了单通道语音分离问题，其中说话人的数量是未知的，并且每个说话人可能说出多个话语。我们提出了一种语音分离模型，通过集成吸引子模块，同时执行分离、动态估计说话人数量并检测个体说话人活动。所提出的系统通过引入一种基于吸引子的架构，有效结合局部和全局时间建模以应对多话语场景，从而优于现有方法。为了在混响和噪声条件下评估该方法，我们通过将Librispeech语音信号与WHAM!噪声信号相结合，合成了一个多说话人多话语数据集。结果表明，所提出的系统能够准确估计声源数量。该系统在已知和未知声源数量的场景中，均能有效检测声源活动并将相应话语分离到正确的输出中。
英文摘要：
This paper addresses the problem of single-channel speech separation, where the number of speakers is unknown, and each speaker may speak multiple utterances. We propose a speech separation model that simultaneously performs separation, dynamically estimates the number of speakers, and detects individual speaker activities by integrating an attractor module. The proposed system outperforms existing methods by introducing an attractor-based architecture that effectively combines local and global temporal modeling for multi-utterance scenarios. To evaluate the method in reverberant and noisy conditions, a multi-speaker multi-utterance dataset was synthesized by combining Librispeech speech signals with WHAM! noise signals. The results demonstrate that the proposed system accurately estimates the number of sources. The system effectively detects source activities and separates the corresponding utterances into correct outputs in both known and unknown source count scenarios.
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。
