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arXiv · 2610.11507

Monaural Continuous-Radius Regional Speech Extraction with Cross-Radius Consistency Learning

Biao Dong·Jie Chen·Jianwei Fang·Wei Xiao·Jiqing Han·Yongjun He
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Abstract

Source-to-microphone distance enables speaker-independent speech extraction without prior enrollment. We propose a monaural continuous-radius regional speech extraction method that directly models the cumulative speech target within a queried radius. To realize continuous region control, the query radius is encoded as a continuous scalar and injected into a time-frequency extraction network, allowing a single model to operate over arbitrary radii within the trained range. Exploiting the nested structure of target-speaker sets across query radii, we introduce cross-radius consistency learning to stabilize predictions for adjacent radii sharing the same nonempty target-speaker set. Experiments on measured RIRs show that continuous-radius conditioning improves selective extraction over discrete conditioning. The proposed method achieves 29.25~dB SI-SDR, 4.40~dB SI-SDRi, 62.01~dB attenuation, and 0.46\% RCE, outperforming fixed-threshold and local-range baselines. It also remains effective with more speakers and additive noise.

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Biao Dong, Jie Chen, Jianwei Fang, Wei Xiao, Jiqing Han, Yongjun He. 2026-10-08. Monaural Continuous-Radius Regional Speech Extraction with Cross-Radius Consistency Learning. https://arxiv.org/abs/2610.11507

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