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arXiv:2609.16396v1 Announce Type: new Abstract: Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior....
arXiv:2606. 11930v2 Announce Type: replace-cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging problem in AI-assisted interview assessment because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
arXiv:2606. 11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
arXiv:2608. 10720v1 Announce Type: new Abstract: Omni-modal dialogue models can understand multimodal inputs and synthesize spoken replies, yet their responses remain visually disembodied.
E-AVI is a new framework for automated video interview assessment that combines verbal, acoustic, and visual data. It extracts timestamped multimodal evidence and uses dimension‑conditioned attention with source‑level embeddings to score candidates. The system also provides a shared evidence pool for natural‑language feedback and follow‑up question answering, outperforming multimodal baselines on RecruitView and a private hospitality dataset.
arXiv:2609.10394v1 Announce Type: cross Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the...