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This research addresses the critical trade-off between low latency and natural speech flow in simultaneous translation, proposing a fluency-aware framework to reduce disruptive pauses without sacrificing quality. By leveraging internal model signals like linguistic diversity, it offers a practical solution for improving user experience in real-time cross-lingual communication systems. The work represents significant progress in optimizing acoustic output generation within the broader field of machine learning applications.
NaturalFlow: Reducing Disruptive Pauses for Natural Speech Flow in Simultaneous Speech-to-Speech Translation
arXiv:2606.13121v1 Announce Type: cross Abstract: Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of consecutive translation. However, the excessive pursuit of low latency often results in fragmented chunk-wise speech. Consequently, listeners are subjected to an unnatural acoustic flow punctuated by frequent pauses, which could increase their cognitive load. To bridge this gap, we introduce a fluency-aware optimization framework designed to discover the sweet spot between the low-latency benefits of simultaneous translation and the natural flow of consecutive translation. Our framework minimizes inter-chunk silences by leveraging model-internal signals, including linguistic diversity and induced temporal variability in speech durations. Experiments on short- and long-form benchmarks show that our framework produces natural speech flow while maintaining competitive latency and translation quality.
Abstract:Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of consecutive translation. However, the excessive pursuit of low latency often results in fragmented chunk-wise speech. Consequently, listeners are subjected to an unnatural acoustic flow punctuated by frequent pauses, which could increase their cognitive load. To bridge this gap, we introduce a fluency-aware optimization framework designed to discover the sweet spot between the low-latency benefits of simultaneous translation and the natural flow of consecutive translation. Our framework minimizes inter-chunk silences by leveraging model-internal signals, including linguistic diversity and induced temporal variability in speech durations. Experiments on short- and long-form benchmarks show that our framework produces natural speech flow while maintaining competitive latency and translation quality.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
arXiv:2606.13121 [cs.CL]
(or arXiv:2606.13121v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13121 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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