arXiv AI
Sep 21

Listen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation

The paper introduces ReACT‑TTS, a two‑stage framework that uses a one‑second sequence of listener facial reactions to plan the emotion and prosody of a conversational utterance before speech synthesis. Experiments on the MELD dataset show that temporally conditioned models outperform text‑only baselines in macro‑F1 and VAD concordance, while accuracy remains unchanged. A contextual‑appropriateness study with speech researchers indicates a strong preference for temporally conditioned responses, and the approach is integrated with a Grad‑TTS backbone for end‑to‑end speech generation.

By Yunji Chu
arXiv Computation and Language
Sep 21

Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction

The paper introduces Omni Demand Understanding (ODU), a benchmark designed to test whether multimodal models can infer a user's underlying demand from complex audio‑visual interactions. ODU requires models to detect the presence of a demand and infer intent using multimodal and conversational context, evaluated across single‑turn and multi‑turn scenarios. The authors built ODU‑Bench through a taxonomy‑guided approach, agentic video generation, and human‑recorded interactions, and found that even top models like Gemini 3.1 Pro recover only 44.7% of key information, with many models exhibiting high false‑trigger rates.

By Qi Chen, Yunfei Chu, Haolin He, Yifan Yang, Zihan Liu, Yuxuan Wang, Ziyang Ma, Ruiyang Xu, Meng Gao, Yinsong Yan, Ling Wang, Hui Wang, Wen Huang, Yiheng Chen, Guanrou Yang, Qiuqiang Kong, Jin Xu, Xie Chen