Papers by Bandhav Veluri

2 papers
AV-Dialog: Spoken Dialogue Models with Audio-Visual Input (2026.acl-long)

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Challenge: AV-Dialog uses audio and visual cues to track the target speaker, predict turn-taking, and generate coherent responses.
Approach: They propose a multimodal dialog framework that uses both audio and visual cues to track the target speaker.
Outcome: AV-Dialog outperforms audio-only models under interference, reducing transcription errors, improving turn-taking prediction and human-rated dialogue quality.
Beyond Turn-Based Interfaces: Synchronous LLMs as Full-Duplex Dialogue Agents (2024.emnlp-main)

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Challenge: Existing spoken dialogue models are half-duplex in nature and require explicit prompting by the user or implicit tracking of interruption or silence events.
Approach: They propose to integrate time information into Llama3-8b so that they run synchronously with the real-world clock.
Outcome: The proposed model outperforms state-of-the-art in dialogue meaningfulness while maintaining naturalness.

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