My accent costs me 30 IQ points on Zoom. So we built an ML model to fix it (krisp.ai)

Co-founder of Krisp here. 1.5B non-native English speakers in the workforce, 4x native — yet all comms infra is optimized for native accents. We spent 3 years building listener-side, on-device accent understanding. The hard parts: no parallel training data exists, the accent space is infinite, accent is entangled with voice identity, and it runs on CPU under 250ms latency. Built in Yerevan, Armenia. Beta is live and free. Happy to go deep on the ML side.

will it help the barista in Starbucks get my name right finally?

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Curious whether wav2vec-style embeddings played a role in your representation learning.

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I would like to use such model but only if it really preserves my voice, otherwise people would understand its not me or I have to use it all the time.

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On-device CPU inference is the real flex here! Optimization probably mattered as much as modeling.

This is a game-changer! I remember each and every call I had with an investor and feeling shy asking "can you repeat?"... thanks krisp, you changed my life!!!

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How did you estimate the number of IQ points?

This is built for international, privacy-first teams!

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This feels adjacent to voice conversion research, but with stricter latency constraints.

The parallel data is a problem here — you can’t crowdsource ground truth because no one can record themselves with a different accent.

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Accent space is effectively infinite. Generalization must rely on invariants rather than enumeration.

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Great work. Natural + clear is the combo that matters.

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