arXiv Machine Learning
Sep 22

AVTR-1: Open Stack for Real-Time Interactive Avatars

arXiv:2609.22913v1 Announce Type: cross Abstract: Talking-head and dyadic models now achieve real-time inference, yet fast motion generation alone does not produce an interactive conversation. A live...

By Artem Kravtsov, Dmitrii Ziganshin, Vsevolod Poletaev, Gleb Balitskiy, Anastasia Tikhonova, Egor Burkov, Vadim Lebedev
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 AI
Sep 7

PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation

PRISM‑Bench is an audio‑centric diagnostic benchmark for text‑to‑audio‑video generation, built from 900 human‑verified samples. It evaluates audio along two axes—audio type (speech, music, sound) and sound‑source visibility (on‑screen vs. off‑screen)—across four perceptual dimensions (audio‑visual coherence, audio quality, audio expressiveness, and prompt following) using 35 fine‑grained criteria. The benchmark employs an enhanced MLLM‑as‑a‑Judge protocol that aligns strongly with human raters, revealing a performance gap between frontier and open‑source T2AV models and highlighting overfitting to perceptual fidelity while struggling with complex grounding and control tasks, especially for music and synchronized on‑screen audio.

By Yuchen Sun, Qian Yang, Jun Wang, Detai Xin, Guoqiao Yu, Guanglu Wan, Qi Jia
arXiv AI
Jun 2

Do Joint Audio-Video Generation Models Understand Physics?

arXiv:2605. 07061v2 Announce Type: replace-cross Abstract: Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate plausible sounds and frames that violate real-world consistency?

By Zijun Cui, Xiulong Liu, Hao Fang, Mingwei Xu, Jiageng Liu, Zexin Xu, Weiguo Pian, Shijian Deng, Feiyu Du, Chenming Ge, Yapeng Tian
arXiv Computation and Language
Oct 5

An automated pipeline for standardised speech-unit annotation in spontaneous dialogue

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.

By Hanlu He, Harald Vilhelm Skat-R{\o}rdam, Ingvi \"Orn\'olfsson, Ivana Konvalinka