{"version":1,"type":"story","url":"https://digestai.news/story/researchers-adapt-speech-language-model-for-simultaneous-translation-u","json":"https://digestai.news/story/researchers-adapt-speech-language-model-for-simultaneous-translation-u.json","markdown":"https://digestai.news/story/researchers-adapt-speech-language-model-for-simultaneous-translation-u.md","slug":"researchers-adapt-speech-language-model-for-simultaneous-translation-u","headline":"Researchers adapt speech language model for simultaneous translation using prefix supervision","summary":"Researchers adapted a full-utterance speech language model for simultaneous speech translation using prefix supervision derived from the model's own complete- and partial-waveform translations. The method requires neither transcripts nor human translations. They compared single-turn forced-prefix and multi-turn append-only decoding, using a confidence threshold to balance quality and latency. On FLEURS and CoVoST2 datasets across three language directions, prefix training improved quality-latency frontiers over the unadapted model. Multi-turn decoding showed stronger performance at low latency, with commit-calibration error falling by 63--68% overall and 68--80% at early prefixes under multi-turn training, while single-turn training provided only modest gains. A small synthesis margin sometimes extended the frontier to lower latency on shorter utterances, but a larger margin degraded quality and calibration.","keyPoints":["Prefix training improves quality-latency frontiers for simultaneous speech translation","Multi-turn decoding reduces commit-calibration error by 63--68% overall and 68--80% at early prefixes","Small synthesis margin aids low latency on short utterances; large margin harms quality"],"whyItMatters":"This work advances real-time speech translation by improving accuracy and latency control without requiring human-transcribed data, potentially expanding access to live translation tools.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-10-05T04:00:00Z","updatedAt":"2026-10-05T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.CL","title":"Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation","url":"https://arxiv.org/abs/2610.02612","publishedAt":"2026-10-05T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Researchers adapt speech language model for simultaneous translation using prefix supervision\", 5 October 2026, https://digestai.news/story/researchers-adapt-speech-language-model-for-simultaneous-translation-u","publisher":"Digest AI","title":"Researchers adapt speech language model for simultaneous translation using prefix supervision","datePublished":"2026-10-05T04:00:00Z","url":"https://digestai.news/story/researchers-adapt-speech-language-model-for-simultaneous-translation-u"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}