{"ID":2875889,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01399","arxiv_id":"2509.01399","title":"CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays","abstract":"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/.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.01399","url_pdf":"https://arxiv.org/pdf/2509.01399v1","authors":"[\"Runduo Han\",\"Yanxin Hu\",\"Yihui Fu\",\"Zihan Zhang\",\"Yukai Jv\",\"Li Chen\",\"Lei Xie\"]","published":"2025-09-01T11:50:43Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.HC\",\"eess.AS\"]","methods":"[]","has_code":false}