{"ID":2847463,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.27198","arxiv_id":"2510.27198","title":"Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm","abstract":"Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially distributed microphones, the choice of reference microphone may have a large influence on the quality of the output signal and the downstream ASR performance. In GSS-based speech enhancement, reference microphone selection is typically performed using the signal-to-noise ratio (SNR), which is optimal for noise reduction but may neglect differences in early-to-late-reverberant ratio (ELR) across microphones. In this paper, we propose two reference microphone selection methods for GSS-based speech enhancement that are based on the normalized $\\ell_p$-norm, either using only the normalized $\\ell_p$-norm or combining the normalized $\\ell_p$-norm and the SNR to account for both differences in SNR and ELR across microphones. Experimental evaluation using a CHiME-8 distant ASR system shows that the proposed $\\ell_p$-norm-based methods outperform the baseline method, reducing the macro-average word error rate.","short_abstract":"Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially distributed microphones, the choice of reference microphone may have a large influence on the quality of the output signal and the downstream A...","url_abs":"https://arxiv.org/abs/2510.27198","url_pdf":"https://arxiv.org/pdf/2510.27198v1","authors":"[\"Anselm Lohmann\",\"Tomohiro Nakatani\",\"Rintaro Ikeshita\",\"Marc Delcroix\",\"Shoko Araki\",\"Simon Doclo\"]","published":"2025-10-31T05:43:11Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.SD\"]","methods":"[]","has_code":false}