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cs.SD updates on arXiv.org

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost Korean aegyo speech shows systematic F1 increase to signal childlike qualities All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation RAS: a Reliability Oriented Metric for Automatic Speech Recognition Speech Enhancement Based on Drifting Models HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition Materialistic RIR: Material Conditioned Realistic RIR Generation SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR ATIR: Towards Audio-Text Interleaved Contextual Retrieval Enhancing Speaker Verification with Whispered Speech via Post-Processing Environmental Sound Deepfake Detection Using Deep-Learning Framework Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India Tadabur: A Large-Scale Quran Audio Dataset
Head, posture, and full-body gestures in unscripted dyadi...
Ľuboš Hládek, Bernhard U. Seeber · 2025-12-03 · via cs.SD updates on arXiv.org

Visual prosody may be critical for communication success in face-to-face conversations in noisy settings. Here, we explore the involvement of hand, head, and whole-body movements, as well as gesturing quality, in dyadic conversations in noisy settings. We hypothesize that increasing background noise would alter the frequency of conversation-related movements to support the roles of the speaker and the listener. Specifically, talkers may increase gesticulation and thus the use of hand, head, trunk, or leg movements more often, while listeners may increase backchanneling or head and trunk movements to improve the signal-to-noise ratio. Additionally, we test whether the synchrony between speech and hand gestures is affected by background noise. Here, pairs of normal hearing participants (n=8) stood in an audiovisual virtual environment while talking freely. The conversational movements were described using a newly developed labeling system with categories that respect their communicative function. The results showed higher gesturing rate during speaking than during listening. Increased levels of background noise led to increased hand-gesture complexity, modulation of head movements, and a change in trunk movements. People spoke 0.7 dB - 1.4 dB louder during hand gesturing in comparison to times with static drop posture but this was unrelated to presence of background noise. The analysis of hand-speech synchrony showed a modest decrease in synchrony for moderate noise level. People adapt their communicative behavior to increased background noise levels by increases in speech production levels and gesturing which may drive additional increase in speech production due to biomechanical coupling; listeners may increase backchanneling to support the exchange and their own signal-to-noise ratio. The synchrony analysis may reflect motivational factors of communication in noisy environments.