SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings

Shuai Wang; Zihan Qian; Ke Zhang; Jiangyu Han; Zikai Liu; Xiaoyang Yu; Haoyu Li; Marc Delcroix; Kai Yu; Lei Xie; Ming Li; Haizhou Li

arXiv · 2607.15198

SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings

Shuai Wang·Zihan Qian·Ke Zhang·Jiangyu Han·Zikai Liu·Xiaoyang Yu·Haoyu Li·Marc Delcroix·Kai Yu·Lei Xie·Ming Li·Haizhou Li

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Abstract

We introduce the REAL-TSE Challenge, an IEEE SLT 2026 satellite challenge on target speaker extraction~(TSE) from real conversational recordings. Given a multi-speaker mixture and one or more enrollment utterances from a target speaker, participating systems must recover only the target speech. Unlike simulated read-speech benchmarks, REAL-TSE evaluates Mandarin and English recordings that contain natural overlap, reverberation, noise, channel mismatch, and conversational dynamics. The challenge defines two complementary tracks: an Online track for low-latency streaming extraction and an Offline track for full-context processing. Systems are evaluated with Token Error Rate (TER), Speaker Similarity (SpkSim), DNSMOS, and target-speaker activity F1. This overview paper describes the task definition, datasets, baselines, evaluation protocol, submitted systems, condition-wise findings, and lessons for future real-world TSE benchmarks.

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BibTeX RIS

Shuai Wang, Zihan Qian, Ke Zhang, Jiangyu Han, Zikai Liu, Xiaoyang Yu, Haoyu Li, Marc Delcroix, Kai Yu, Lei Xie, Ming Li, Haizhou Li. 2026-07-16. SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings. https://arxiv.org/abs/2607.15198

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