NaturalFlow Framework Reduces Disruptive Pauses in Real-Time Speech Translation
Researchers have developed NaturalFlow, a framework that improves the naturalness of simultaneous speech-to-speech translation by reducing pauses between speech chunks while maintaining low latency. The system uses linguistic diversity and temporal variability signals to optimize the balance between real-time translation speed and natural speech flow. This addresses a key usability challenge in simultaneous translation systems, which traditionally sacrifice speech naturalness for speed.
NaturalFlow is a fluency-aware optimization framework designed to improve simultaneous speech-to-speech translation—a technology that enables near-real-time communication by minimizing latency compared to consecutive translation. The core problem the research addresses is that aggressive latency reduction often produces fragmented, choppy speech with frequent unnatural pauses that increase listener cognitive load. The framework minimizes inter-chunk silences by leveraging model-internal signals, specifically linguistic diversity and induced temporal variability in speech durations. Experiments on both short- and long-form benchmarks demonstrate that NaturalFlow achieves natural speech flow while maintaining competitive latency and translation quality. The work represents a published long paper at the 26th Interspeech Conference, a major venue for speech processing research.
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- arXiv cs.AICenter
NaturalFlow: Reducing Disruptive Pauses for Natural Speech Flow in Simultaneous Speech-to-Speech Translation
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