On January 21, 2015, I saw an email in my inbox about an issue of Berkeley Engineering, which must be some magazine published by the university every few months. I wasn’t planning on reading it in detail, but one of the articles caught my eye. It was about a former Berkeley graduate student, Thibault Duchemin, who had just co-founded a company called Transcense (now named Ava) to break the communication barrier that plagues hearing impaired people when we attempt to talk with hearing people. Their main product is an app that can perform automatic speech recognition, so a hearing impaired person can look at his/her phone during a conversation and (hopefully) read the text to understand what’s going on.

Why did Thibault start the company? In part, it was because of his experience as a hearing person in a deaf family. (That’s rather unusual, since it’s typically the case that there’s a single deaf person in a hearing family1.)

When I was reading this article, I kept thinking about the continued importance of automatic speech recognition. Today, it is widely used in practice (as any avid Googler can tell) and is also a popular research subfield in computer science. I wish I could do research in that area, but unfortunately, I don’t think people who do that kind of research would be interested in working with me.

Needless to say, I wanted to know more, so I sent Thibault an email, and was pleasantly surprised to get a fast response. We decided to meet in person at one of my favorite cafes, Nefeli’s Cafe, located on the edge of the Berkeley campus. We chatted for about an hour in sign language. I was probably a little rusty, and there may have been some French versus English signing confusion, but we understood what we were saying to each other.

I later met a few more people from Ava since I asked to stay in touch with them. Since my meeting with Thibault, they’ve made enough progress on their product that it’s currently in beta stage and released to a specific audience. I recently tested it out with one of their other co-founders, Pieter, and they’ve definitely made progress, though they need to hone out some of the bugs we found during my session. They only have about nine people working for them so hopefully they will be able to work hard to get the app in a useful stage. By the way, here’s the link their new website.

One might wonder how their product works. I don’t know the details, but I think they use some of Google’s speech recognition software. It’s possible to design your own automatic speech recognition software (I did one for CS 288 using Hidden Markov Models) but it’s definitely far easier to use one that’s already existing, rather than build a huge one from scratch, which would require a ridiculous amount of data, and probably lots of neural network tweaking.

As I continue to fight daily doses of isolation, it’s nice to think in the back of my mind that there are people out there willing to work and help me.

  1. That doesn’t apply to me, however. ↩