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arXiv:2610.27109v1 Announce Type: cross Abstract: Spoken interaction is moving from alternating turns to full-duplex conversation, where an agent listens while speaking. Learning this coordination re...
arXiv:2606.11167v2 Announce Type: replace Abstract: Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, cur...
arXiv:2609.13117v1 Announce Type: new Abstract: Full-duplex evaluation often emphasizes whether an agent keeps speaking or stops. That binary cannot express a third response humans use routinely: con...
The paper introduces a pipeline that generates intent‑labeled, two‑channel conversational speech from relational event lists, enabling controlled synthesis of full‑duplex dialogue with 42 phenomena across eight families in English and Mandarin. By having an LLM author each event’s speaker, text, conversational act, and attachment, and then aligning and timing these events independently, the system produces diverse, realistic turn‑taking signals. Experiments show that models trained on this synthetic corpus achieve higher floor‑occupancy accuracy and better start‑speaking/listening F1 scores compared to models trained on prior data.
arXiv:2610.34991v1 Announce Type: new Abstract: End-to-end full-duplex spoken dialogue models enable natural, real-time spoken interactions with interruptions, backchannels, and overlapping speech. P...
The paper introduces an automated pipeline that extracts conversational turns and backchannels from separate-channel recordings of spontaneous dyadic dialogue, combining voice activity detection, channel-energy filtering, temporal merging, automatic speech recognition, and context-based post‑processing. Evaluated on 99 ten‑minute Danish conversations, the system achieved F1 scores around 0.62 for both turns and backchannels, with median onset/offset errors of roughly 0.15–0.18 s. Performance was consistent across normal and asymmetric listening conditions, and a case study showed the pipeline’s outputs were less variable than human annotations, supporting its use as a reliable first‑pass annotation tool in semi‑automated workflows.