arXiv:2609.17360v1 Announce Type: new Abstract: Full-duplex spoken dialogue systems must distinguish interruptions that require yielding the floor from backchannels that permit continued speaking. Existing benchmarks typically evaluate events independently and may therefore rewar…
arXiv cs.CL
TIER_1English(EN)·Ke Hu, Nourchene Ferchichi, Edresson Casanova, Ankita Pasad, Elena Rastorgueva, Chen Chen, Nithin Rao Koluguri, Piotr Zelasko, Yifan Peng, Hainan Xu, Zhehuai Chen, Boris Ginsburg·
arXiv:2609.15759v1 Announce Type: new Abstract: Full-duplex speech-to-speech (S2S) models enable natural conversational AI by allowing simultaneous listening and speaking. However, these models typically lack inherent user speech transcription, which is essential for applications…
arXiv:2609.12872v1 Announce Type: new Abstract: We present DuplexDrama, the first synthesized spoken dialogue dataset that simultaneously covers four dimensions: (i) complete persona and scenario settings; (ii) three full-duplex behaviors (interruption, backchannel, incomplete); …
arXiv cs.CL
TIER_1English(EN)·Utkarsh Tyagi, Ramaneswaran Selvakumar, Advait Gosai, Sonal Kumar, Nikhil Barhate, Isabell Sagar, Steven Li, Miheer Bavare, Daniel Quigley, Fabiola Tapia Carrillo, Jose M Patron E, Diego Mac\'ias Guti\'errez, Paul Song, Ramani Duraiswami, Dinesh Manocha,…·
Full-duplex speech models require training data that preserves turn-taking, overlap, interruption, and backchannel behavior, yet these signals are entangled across speakers in noisy real-world recordings. We present Conversational Voice, a pipeline that converts real two-speaker …