HumanOmni-Speaker: Identifying Who said What and When
While Omni-modal Large Language Models have made strides in joint sensory processing, they fundamentally struggle with a cornerstone of human interaction: deciphering complex, multi-person conversational dynamics to accurately answer ``Who said what and when.'' Current models suffer from an ``illusion of competence'' -- they exploit visual biases in conventional benchmarks to bypass genuine cross-modal alignment, while relying on sparse, low-frame-rate visual sampling that destroys crucial high-frequency dynamics like lip movements. To shatter this illusion, we introduce Visual-Registered Speaker Diarization and Recognition (VR-SDR) and the HumanOmni-Speaker Benchmark. By strictly elimina...
| Authors | Detao Bai, Zhiheng Ma, Xihan Wei |
|---|---|
| Primary category | cs.CV |
| Published | 23 March 2026 |
| Last updated at source | 7 July 2026 |
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