评测真实场景下视听语音增强效果，发布基准模型与评估工具。

Audio-visual speech enhancement (AVSE) uses visual-speech cues from a target speaker to recover that speaker's speech from noisy or overlapping speech. Many widely used protocols construct mixed signals from separately recorded audio sources and assume reliable video, leaving their performance under natural overlap and visual failure insufficiently characterized. The Real-World AVSE Challenge evaluates two related settings. Track~1 comprises two scenarios: real-world mixtures recorded with two speakers speaking simultaneously, without a corresponding clean reference signal, and synthetic remixes obtained by manually mixing the separately recorded speech of two speakers, with a clean reference signal available; Track~2 reuses audio but pairs it with a degraded target video and contains additional 3-m far-field recordings. The speakers in the development and test sets are disjoint. Evaluation metrics include clean-waveform fidelity, learned quality estimates, transcription accuracy, and speaker identification. In the remix task on the development set, the baseline model achieved an SI-SDR of $-4.069$~dB and an STOI of $0.388$ on Track~1, and an SI-SDR of $-2.851$~dB and an STOI of $0.470$ on Track~2. We release the AV-ConvTasNet checkpoints, the offline evaluator, and the official baseline results on the development and test sets.