mlsanityData to 19 Oct 2026

Identity-Faithful Audio-Visual Target Speaker Extraction with REAL-2MIX and VOXBLINK2-AVSE

arXiv 2608.03964 ↗First posted 4 Aug 2026eess.AScited by 1

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Abstract (arXiv)

Audio-visual target speaker extraction should return the speaker indicated by the video, yet a separator can ignore the visual cue and repeatedly output the acoustically dominant voice. We introduce REAL-2MIX, a Mandarin AV-TSE benchmark of jointly recorded real two-speaker mixtures with synchronized multi-view video. Each scene also contains preceding A-only and B-only stages that provide in-scene speaker references. It contains 77 scenes and 7,598 clips (11.84 hours), including 6,042 dual-annotated mixtures. After processing there leave 6,038 evaluable mixtures and 12,076 target-speaker rows. We additionally curate VOXBLINK2-AVSE from VoxBlink2, comprising 250,828 synchronized audio--lip-ROI pairs from 28,421 identities and 766.17 hours of speech. Our extractor uses frozen, 1,280-dimensional projected AV-HuBERT features, target-conditioned training, and layer-wise feature modulation. We jointly evaluate content with Qwen3-ASR-1.7B CER and target identity with WeSpeaker ResNet34 plus Overlapped Speech Detection (OSD). On the complete manifest, the best archived checkpoint obtains 0.2261 CER, 82.22% strict output correctness, and 69.53% both-output strict success.

Key facts. Cited by 1 paper in the map (data through 19 Oct 2026). First posted on arXiv 4 Aug 2026.

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  5. 2024VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
  6. 2023VoxBlink: A Large Scale Speaker Verification Dataset on Camera
  7. 2026PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction
  8. 2025Enhancing Target Speaker Extraction with Explicit Speaker Consistency Modeling ↗