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There are a million and one services for voice transcription on the market. But even with just one job to do, I’ve never seen a service that can handle the long tale of vocabulary used in the real world. This is particularly challenging if you’re a startup trying to sell your service to enterprises that rely on accurate transcription for their operations.
Jon Goldsmith, co-founder of Tetra, a voice transcription startup, understands this challenge — in fact he is even willing to admit that he hasn’t 100 percent cracked the problem. But Goldsmith believes the answer lies in deep learning and he’s setting out to prove it with a $1.5 million seed round led by Amplify Partners, with participation from Y Combinator and a number of angels.
Tetra - Office - Goldsmith - Nik - Liolios
I dropped by the Tetra office to check out what Goldsmith, his co-founder Nik Liolios and one other engineer had created. Goldsmith gave me a call using his smartphone with the Tetra app installed. As he and the deep learning models running in the background listened, I threw out a barrage of challenges for the transcription service.
Speaking at varying speeds, throwing out numbers, startup names and other tough words did stump Tetra to some degree — but to be fair there is no AI that I haven’t broken. Given how easy Tetra is to use, I could see it being used as a backup reference or for record keeping — turn it on, forget about it and use it to search through notes later.
Cases - Percent - Accuracy - Tetra - Transcription
In cases where 99 or 100 percent accuracy is required, Tetra offers human transcription for...
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