AVSRBench: A Multi-Condition AVSR Benchmark
While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true generalization or just domain adaptation. To investigate this gap, we evaluate three AVSR architectures across six conditions: controlled broadcast speech, fixed-grammar utterances, hyper-articulated Lombard speech, read speech from professional lipspeakers and non-professional speakers, and spontaneous multi-party video conversations. We find that visual-only performance deteriorates rapidly beyond broadcast domains, and audio-video fusion mainly benefits Lombard speech environments. Visual understanding degrades sharply at 90° profile views...
| Authors | Rishabh Jain, Naomi Harte |
|---|---|
| Primary category | eess.AS |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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