My accent costs me 30 IQ points on Zoom. So we built an ML model to fix it(krisp.ai)
krisp.ai
My accent costs me 30 IQ points on Zoom. So we built an ML model to fix it
https://krisp.ai/blog/introducing-accent-conversion-for-the-listener/
56 comments
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.
What do you think about the misuse potential (by scammers for example)?
Aside from that, I like that this exists now.
Aside from that, I like that this exists now.
This is for listener-side, not speaker-side.
So no misuse case here.
Edge-side CPU inference is the quiet power move. Feels like the engineering grind on optimization carried just as much weight as the model architecture itself.
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!!!
The lack of parallel accent data makes this fundamentally unsupervised. Curious if this leans more on latent disentanglement than direct supervision.
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.
Nice to finally see this direction of accent conversion (that is on incoming calls) in the Krisp app. This is a very meaningful feature.
The parallel data is a problem here — you can’t crowdsource ground truth because no one can record themselves with a different accent.
Really cool to see accent adaptation in real time — curious about benchmarks and how well this handles messy, real Zoom calls
How did you estimate the number of IQ points?
Identity preservation seems harder than accent mapping itself. How do you measure that rigorously?
Latency can destroy conversational rhythm. What’s your p95 inference time?
also are there any benchmarks we can see?
Identity preservation seems harder than accent mapping itself. How do you measure that rigorously?
Without full utterance context, homophones must be tricky. How do you avoid semantic drift?
Accent space is effectively infinite. Generalization must rely on invariants rather than enumeration.
On-device CPU inference is the real flex here. Optimization probably mattered as much as modeling.
Without full utterance context, homophones must be tricky. How do you avoid semantic drift?
On-device CPU inference is the real flex here! Optimization probably mattered as much as modeling.
This feels adjacent to voice conversion research, but with stricter latency constraints.
Local CPU inference stands out. Careful optimization likely rivaled the modeling effort.
Curious whether wav2vec-style embeddings played a role in your representation learning.
This feels adjacent to voice conversion research, but with stricter latency constraints.
Curious whether wav2vec-style embeddings played a role in your representation learning.
Yeh, this would be helpful for the Singlish friends of mine out there!
Can users control the degree of accent modification?
Great work. Natural + clear is the combo that matters.
This is built for international, privacy-first teams!
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will it help the barista in Starbucks get my name right finally?
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