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arXiv · 2606.23332

Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement

Mads Østergaard·Alexander Neergaard Zahid·Karl Ulbæk·Andreas Hansen Bagge·Kenny Falkær Olsen·Rasmus Malik Høegh Lindrup
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Abstract

We introduce own-voice cancellation (OVC): removing a target (enrolled) speaker from a noisy multi-speaker mixture while preserving any remaining speech. Framed as the complement of target speaker extraction, OVC addresses latency-induced own-voice artifacts that arise when a far-field device streams enhanced audio back to the user, as the round-trip time easily exceeds the perceptual threshold for own-voice distortion. We condition a time-domain model with only 2 ms algorithmic latency on a short enrollment utterance and benchmark TD-SpeakerBeam alongside a lighter Mamba-MinGRU masker built from Mamba blocks with MinGRU temporal mixing. Replacing the ConvTasNet-based auxiliary network with a linear RNN encoder improves both signal-to-distortion ratio and predicted MOS while reducing compute. Results establish OVC as a practical, low-latency enhancement objective for far-field denoising.

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Mads Østergaard, Alexander Neergaard Zahid, Karl Ulbæk, Andreas Hansen Bagge, Kenny Falkær Olsen, Rasmus Malik Høegh Lindrup. 2026-06-22. Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement. https://arxiv.org/abs/2606.23332

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