Language & audio
A robot that takes instructions has to hear them in a real room: with echo, machinery, overlapping speakers and an accent it was not trained on. Clean speech data is not found. It is made.
Speech models fail in a particular way: not by producing nonsense, but by producing something fluent and wrong. A mistranscription that reads perfectly is invisible to an automatic quality check and invisible to a reviewer who does not speak the language. This is why transcription remains stubbornly human work at the quality end, and why the languages with the least data are exactly the ones where the errors are hardest to catch.
What this needs from people
- Verbatim transcription that survives noise, accent and crosstalk.
- Segment and speaker boundaries aligned to the audio, not approximated.
- Cleanup and editing so a training set is consistent rather than merely large.
- Native-language judgement: the errors that matter are the ones a non-speaker cannot hear.
The research
Where this subsystem actually stands.
Talks & demonstrations
The people building it, in their own words.
OpenAI Whisper — paper walkthrough
Vision-Language-Action Models — OpenVLA, π0, RT-2, Gemini Robotics
Where HSV fits
HSV runs German ASR transcription and audio editing as standing delivery lines. Both are staffed by trained reviewers rather than a single pass of automatic output, because the failure mode that breaks a speech model is the plausible mistranscription nobody flagged.
See the delivery line