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New method improves simultaneous speech translation quality-latency trade-off

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for end-to-end simultaneous speech translation that improves the quality-latency trade-off by adapting a full-utterance speech language model with prefix supervision. This approach, tested on FLEURS and CoVoST2 datasets across three language directions, uses confidence thresholds to manage inference-time performance and explores both single-turn and multi-turn decoding strategies. The multi-turn decoding, in particular, significantly reduces commit-calibration error, especially at early prefixes, leading to more consistent and accurate translations. AI

IMPACT Enhances real-time translation capabilities by improving accuracy and reducing latency in speech-to-text systems.

RANK_REASON Academic paper detailing a new method for speech translation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method improves simultaneous speech translation quality-latency trade-off

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Academic paper detailing a new method for speech translation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hieu Hoang, Amittai Axelrod ·

    Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation

    arXiv:2610.02612v1 Announce Type: new Abstract: Simultaneous speech translation must emit useful target text before the source is complete while preserving every committed token. We adapt a full-utterance speech language model using prefix supervision derived from its own complet…

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