mlsanityData to 19 Oct 2026

Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm

arXiv 2510.27198 ↗First posted 31 Oct 2025eess.AS cs.SDcited by 0

On the map: Speech and audio language models → Speech enhancement and noise suppression

Abstract (arXiv)

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 ℓp-norm, either using only the normalized ℓp-norm or combining the normalized ℓp-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 ℓp-norm-based methods outperform the baseline method, reducing the macro-average word error rate.

Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models. Builds on 10 of its 23 references that are in the map. First posted on arXiv 31 Oct 2025.

Builds on

10 of 23 references.

References · 10

  1. 2021DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressorscited by 292
  2. 2021NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasetscited by 270
  3. 2023TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudiocited by 82
  4. 2024SCOREQ: Speech Quality Assessment with Contrastive Regressioncited by 49
  5. 2024VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Musiccited by 44
  6. 2024The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarizationcited by 27
  7. 2024STCON System for the CHiME-8 Challengecited by 9
  8. 2023The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR Systemcited by 8
  9. 2021Learning to Rank Microphones for Distant Speech Recognitioncited by 5
  10. 2024Reference Microphone Selection for the Weighted Prediction Error Algorithm using the Normalized L-p Normcited by 1

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  1. 2023Guided Speech Enhancement Network
  2. 2025Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
  3. 2026Generating Training Targets for Real-World Speech Enhancement via Close-to-Distant Microphone Projection
  4. 2024Reference Channel Selection by Multi-Channel Masking for End-to-End Multi-Channel Speech Enhancement
  5. 2026When Audio Separation Hurts Zero-Shot ASR: Evaluating SAM-Audio with Whisper on Bengali and English Speech
  6. 2022A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation
  7. 2025ReFESS-QI: Reference-Free Evaluation For Speech Separation With Joint Quality And Intelligibility Scoring
  8. 2024Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition