Live interpreting
A useful interpreter keeps the audience with the speaker.
Correct words are part of the job. The listener also needs the right meaning, at a pace they can follow.
What the interpreting research adds.
In Toward Machine Interpreting, Matthias Sperber, Maureen de Seyssel, Jiajun Bao, and Matthias Paulik examine human interpreting studies. Their paper is a research perspective, not a product comparison or proof that AI has surpassed people.
They argue that a useful interpreter must preserve intent, communicate clearly, and make listening comfortable. Timing matters: speaking too early can lose context, while falling behind makes the event harder to follow. Uncertainty and recovery from errors are part of the job too.
For Baian, that suggests testing complete listening experiences alongside translation accuracy. It does not justify guessing missing speech or silently changing a speaker’s claims.
Three tests we would run.
Meaning under pressure
Use dialects, interrupted sentences, language switches, unfamiliar names, and numbers. Ask bilingual reviewers to mark missing or added meaning. Check quotations against their original sources.
Delay at the listener’s phone
Record the source speech and listener output together. Measure time to readable translation and time to audible translation separately. Repeat during a longer talk to catch delays that accumulate.
Can the audience follow?
Ask listeners short comprehension questions after the talk. Note confusing wording, distracting text changes, rushed audio, and anything they missed. A fluent sentence is not enough if the point never reaches them.
Where public benchmarks help.
FLEURS provides multilingual speech for a consistent starting point. Read speech is useful for basic checks, but does not replace tests of spontaneous dialects or room audio.
IWSLT 2026, co-authored by Sperber, reports newer shared tasks in speech translation and evaluation. It is relevant follow-on work, not a second edition of the interpreting paper.
OpenSTBench evaluates meaning alongside speech quality, speaker preservation, emotion, timing, and delay. Those dimensions help define a broader test; they do not establish Baian’s performance.
The tests above are proposed work. No new Baian benchmark results are reported on this page.
Sources
Sources for this guide.
About these notes
Written by Baian from the linked research. We have not rerun these papers’ experiments, and this is not an independent review of our product.
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