Krisp Accent Conversion - Understand accented speech in real time
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Accent Conversion for the Listener removes accent friction in real time. It converts accented English into neutral American English on the listener’s side, so speakers don’t change how they talk — you just understand instantly. Fully on-device with near-zero latency and works across Zoom, Teams, and Meet. Built for global teams where “can you repeat that?” quietly slows everything down.
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This is DOPE! Just like others, would love to see a Chrome extension. Congrats!
I’ve been looking for a solution like this, and I'm quite impressed by the demo showing such low latency in real-time audio processing.
As a developer myself, the seamless UX really stands out. Was it a big challenge to integrate the AI summary feature into the existing desktop app architecture? Looking forward to trying it out!
Useful, I recently traveled to India and had problems understanding people, having to ask them a couple of times to repeat what they just said and felt uncomfortable doing that. Can this be used for non-work related situations as well?
This has been a long build. Accent Conversion is one of the most practical features we’ve shipped. Thanks for checking it out.
Listener-side is the right call here. Making adaptation happen on the receiver rather than forcing speakers to modify their voice is both more ethical and more practical at scale.
The SDK angle for embedding this directly into voice AI agents is what makes this genuinely new territory like not just a meeting tool upgrade. How are you thinking about measuring comprehension improvements in voice agent deployments specifically ie whether accent-related recognition errors drop, or is that still mostly a human-reported metric?
Game changer for international teams. Call center SaaS is a competitive ad market — how are you positioning against the noise?
Running this on the listener side is a smart design choice. The speaker doesn't have to install anything or feel like they're being "corrected," which would kill adoption immediately.
One thing I'm wondering about: does the conversion affect the emotional register of speech? Accents carry a lot of tonal information beyond just phonemes. If someone is frustrated or excited, does that come through the conversion intact?
The call center use case makes a lot of sense. Reducing repetition ("sorry, can you say that again?") probably does more for customer satisfaction than any script optimization.
The accent conversion feature is the one that stands out to me that's a genuinely hard problem and if it works well in real time it could be transformative for global teams where communication friction from accents causes real misunderstandings. Curious how it handles edge cases like someone switching between languages mid-sentence or strong regional accents within the same language like Scottish English versus American English. Also the no meeting bots angle is the right call nobody wants a robot joining their call and making everyone self conscious. How does the transcription accuracy hold up in a meeting with five or six people talking over each other?
Real-time accent conversion is a massive unlock for global teams. The accented speech problem isn't just about clarity, it's about the cognitive load on listeners, which affects meeting productivity. How does it handle code-switching, like when someone moves between their native language and English mid sentence? And is the conversion noticeable to the speaker themselves, or is it fully transparent?