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The paper introduces FastTurn, a framework for low-latency turn detection in spoken dialogue systems that combines streaming CTC decoding with acoustic features. This approach enables early turn-taking decisions from partial observations while preserving semantic cues, addressing the limitations of purely acoustic or ASR-based methods. Experiments on a new, realistic human dialogue dataset demonstrate that FastTurn achieves higher accuracy and lower interruption latency compared to baselines, while also exhibiting robustness to challenging acoustic conditions.
Achieve faster, more accurate turn-taking in spoken dialogue by fusing streaming speech recognition with raw audio cues – even when it's noisy.
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.
