标题： 舱室分离：基于掩码的MVDR实时车内语音分离方法，适用于分布式异构阵列 显示英文标题

标题： CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays

摘要： 从多个说话人中分离重叠语音对于有效的人车交互至关重要。 本文提出CabinSep，一种轻量级基于神经掩码的最小方差无失真响应（MVDR）语音分离方法，以减少后端自动语音识别（ASR）模型中的语音识别错误。 我们的贡献有三个方面：首先，我们利用通道信息提取空间特征，这提高了语音和噪声掩码的估计。 其次，我们在推理过程中使用MVDR，减少语音失真，使其更符合ASR需求。 第三，我们引入一种结合模拟和真实记录的脉冲响应（IRs）的数据增强方法，提高了区域边界处的说话人定位，并进一步减少了语音识别错误。 计算复杂度仅为0.4 GMACs， CabinSep在真实记录的数据集上相比最先进的DualSep模型实现了17.5%的语音识别错误率相对降低。 演示视频可在以下网址查看：https://cabinsep.github.io/cabinsep/。 显示英文摘要

摘要： Separating overlapping speech from multiple speakers is crucial for effective human-vehicle interaction. This paper proposes CabinSep, a lightweight neural mask-based minimum variance distortionless response (MVDR) speech separation approach, to reduce speech recognition errors in back-end automatic speech recognition (ASR) models. Our contributions are threefold: First, we utilize channel information to extract spatial features, which improves the estimation of speech and noise masks. Second, we employ MVDR during inference, reducing speech distortion to make it more ASR-friendly. Third, we introduce a data augmentation method combining simulated and real-recorded impulse responses (IRs), improving speaker localization at zone boundaries and further reducing speech recognition errors. With a computational complexity of only 0.4 GMACs, CabinSep achieves a 17.5% relative reduction in speech recognition error rate in a real-recorded dataset compared to the state-of-the-art DualSep model. Demos are available at: https://cabinsep.github.io/cabinsep/.