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Offline speech translation system self-corrects on edge devices

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an offline speech-to-speech translation system that can run on a Jetson Nano edge device and self-correct its translations without needing retraining. The system utilizes a Whisper-tiny ASR model and an OPUS-MT translator, with a multilingual BERT model acting as a Quality Estimation gate to trigger secondary correction passes. Experiments on English-Spanish translation showed that Minimum Bayes-Risk decoding improved translation quality significantly, while a Quality Estimation model was more effective as a gate than a ranker, freeing up memory. AI

IMPACT Enables real-time, offline speech translation on low-power devices, potentially improving accessibility and usability in remote or resource-constrained environments.

RANK_REASON The cluster contains an 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 →

Offline speech translation system self-corrects on edge devices

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The cluster contains an 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) · Zubair Ajmal Farooq, Diptesh Kanojia ·

    Quality-Aware Self-Correcting Speech Translation on an Edge Device

    arXiv:2610.07545v1 Announce Type: new Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retraining. A Whisper-tiny ASR feeds an Opus-MT translator; multilingual BERT cosine s…

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