← 研踪 PaperTrailarXiv:2607.11083 · v2
The SonicAGI System for the REAL-TSE Challenge
首次提交 2026-07-13 12:53
版本更新 2026-10-27 11:39
雷达观测 2026-10-29 00:35
摘要
Real-world target speaker extraction (TSE) remains challenging because target speech, interference, and enrollment are recorded under mismatched acoustic conditions with reverberation, noise, and irregular conversational overlap. This paper describes the SonicAGI submission to the REAL-TSE Challenge (IEEE SLT 2026). We take a data-centric approach that combines fully simulated mixtures from clean speech with real meeting overlaps, and use a frozen offline enhancer to provide a denoised mirror of real targets for auxiliary supervision. For the online track, we introduce SwiftNet-Lookahead, which inserts a single bounded-lookahead module before a strictly causal iterative separator and keeps the total system latency at 96 ms. For the offline track, we use a frame-level enrollment cross-attention USEF-TFGridNet with a magnitude-domain fusion stage designed to balance perceptual quality and speaker fidelity. In the final verified Phase-1 ranking, SwiftNet-Lookahead ranks third in Track 1 and USEF-TFGridNet ranks fifth in Track 2, both exceeding the challenge baselines. These results suggest that real-data-oriented training and track-specific modeling are effective for conversational TSE.
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v2 · 2026-10-27 11:39 · 最新观测版本
The SonicAGI System for the REAL-TSE Challenge
Real-world target speaker extraction (TSE) remains challenging because target speech, interference, and enrollment are recorded under mismatched acoustic conditions with reverberation, noise, and irregular conversational overlap. This paper describes the SonicAGI submission to the REAL-TSE Challenge (IEEE SLT 2026). We take a data-centric approach that combines fully simulated mixtures from clean speech with real meeting overlaps, and use a frozen offline enhancer to provide a denoised mirror of real targets for auxiliary supervision. For the online track, we introduce SwiftNet-Lookahead, which inserts a single bounded-lookahead module before a strictly causal iterative separator and keeps the total system latency at 96 ms. For the offline track, we use a frame-level enrollment cross-attention USEF-TFGridNet with a magnitude-domain fusion stage designed to balance perceptual quality and speaker fidelity. In the final verified Phase-1 ranking, SwiftNet-Lookahead ranks third in Track 1 and USEF-TFGridNet ranks fifth in Track 2, both exceeding the challenge baselines. These results suggest that real-data-oriented training and track-specific modeling are effective for conversational TSE.
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