← 研踪 PaperTrailarXiv:2611.01757 · v1
Spatially-Guided Speaker Extraction Based on Relative Position Cues
首次提交 2026-11-01 18:02
版本更新 2026-11-01 18:02
雷达观测 2026-11-04 23:43
摘要
We present a multi-channel target speaker extraction (TSE) framework that is guided by relative spatial cues rather than explicit speaker-dependent references, such as clean enrollment utterances or precise directions of arrival (DOA). The proposed approach encodes the target speaker through discrete, interpretable relations between concurrent speakers, such as left and right or near and far, referred to as the Relative Position Cue (RPC). This relational conditioning effectively leverages spatial information while avoiding explicit DOA estimation, thereby enabling more robust operation under realistic conditions. We evaluate the proposed method across multiple datasets encompassing diverse acoustic conditions, including directional and diffuse babble noise, varying numbers of microphones, mixtures with different numbers of speakers, and dynamic scenarios involving moving speakers. Experimental results demonstrate that the RPC-based approach consistently achieves strong extraction performance and generalizes well across all tested configurations, establishing it as a practical and interpretable alternative for spatially informed speaker extraction.
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v1 · 2026-11-01 18:02 · 最新观测版本
Spatially-Guided Speaker Extraction Based on Relative Position Cues
We present a multi-channel target speaker extraction (TSE) framework that is guided by relative spatial cues rather than explicit speaker-dependent references, such as clean enrollment utterances or precise directions of arrival (DOA). The proposed approach encodes the target speaker through discrete, interpretable relations between concurrent speakers, such as left and right or near and far, referred to as the Relative Position Cue (RPC). This relational conditioning effectively leverages spatial information while avoiding explicit DOA estimation, thereby enabling more robust operation under realistic conditions. We evaluate the proposed method across multiple datasets encompassing diverse acoustic conditions, including directional and diffuse babble noise, varying numbers of microphones, mixtures with different numbers of speakers, and dynamic scenarios involving moving speakers. Experimental results demonstrate that the RPC-based approach consistently achieves strong extraction performance and generalizes well across all tested configurations, establishing it as a practical and interpretable alternative for spatially informed speaker extraction.
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