Researchers adapt speech language model for simultaneous translation using prefix supervision
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…
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Key points
- 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
Read the original at arXiv cs.CL · by Hieu Hoang, Amittai Axelrod primary sourceOpen source ↗
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