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Candor-LR: A Dyadic Conversational Dataset for Audio-Visual Speech Recognition
Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the complexity of natural conversation, which involves overlapping speech, spontaneous turn-taking, unscripted vocabulary and variable acoustic conditions. To shift the field toward realistic dialogue, we introduce Candor-LR, a conversational benchmark derived from the CANDOR corpus of 1,656 natural dyadic videoconferences. Our custom data preparation pipeline yields 713.5, 10.1, and 60.1 hours of training, validation, and test data, respectively. Eva
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T16:14:29.000Z
First collected: 2026-09-20T19:32:24.350Z. This is not the publication date.