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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? 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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 Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation Virtual boundary integral neural network for three-dimensional exterior acoustic problems Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use AST: Adaptive, Seamless, and Training-Free Precise Speech Editing Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs Hierarchical Codec Diffusion for Video-to-Speech Generation ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench 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Fine-Tuned Speech Representation Models For Cross-Task Generalization Throat and acoustic paired speech dataset for deep learning-based speech enhancement Dementia classification from spontaneous speech using wrapper-based feature selection DASB - Discrete Audio and Speech Benchmark Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
FastTurn: Unifying Acoustic and Streaming Semantic Cues for Low-Latency and Robust Turn Detection
[Submitted on 2 Apr 2026 (v1), last revised 30 Jun 2026 (this ve · 2026-04-02 · via cs.SD updates on arXiv.org

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Abstract:Recent advances in AudioLLMs have enabled spoken dialogue systems to move beyond turn-based interaction toward real-time full-duplex communication, where the agent must decide when to speak, yield, or interrupt while the user is still talking. Existing full-duplex approaches either rely on voice activity cues, which lack semantic understanding, or on ASR-based modules, which introduce latency and degrade under overlapping speech and noise. Moreover, available datasets rarely capture realistic interaction dynamics, limiting evaluation and deployment. To mitigate the problem, we propose \textbf{FastTurn}, a unified framework for low-latency and robust turn detection. To advance latency while maintaining performance, FastTurn combines streaming CTC decoding with acoustic features, enabling early decisions from partial observations while preserving semantic cues. We also release a test set based on real human dialogue, capturing authentic turn transitions, overlapping speech, backchannels, pauses, pitch variation, and environmental noise. Experiments show FastTurn achieves higher decision accuracy with lower interruption latency than representative baselines and remains robust under challenging acoustic conditions, demonstrating its effectiveness for practical full-duplex dialogue systems.

Submission history

From: Chengyou Wang [view email]
[v1] Thu, 2 Apr 2026 11:00:37 UTC (198 KB)
[v2] Tue, 7 Apr 2026 06:30:18 UTC (198 KB)
[v3] Sat, 18 Apr 2026 13:20:42 UTC (198 KB)
[v4] Mon, 27 Apr 2026 09:09:06 UTC (198 KB)
[v5] Tue, 30 Jun 2026 12:44:47 UTC (197 KB)

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