It happens in every meeting, phone call, or interview–that horrifying moment when you realize you’ve fallen behind in your notes and missed something crucial. You try everything from repeating the last thing you remember over and over to rubbing memory-jogging essential oils on your wrists, but it’s all for naught. The conversation is lost. Forgotten.
But what if you never had to worry about missing a word? What if you could get a thorough read out of everything said in your weekly sit-down with your boss without worrying about writing down a single thing? With TranscribeMe, this vision is becoming a reality. Via a complex interaction between human scribes and continuously growing AI technology, TranscribeMe offers better, faster transcription services at unprecedented low cost.
We recently spoke with TranscribeMe CEO Alexei Dunayev about how their technology is forging a new frontier in transcription services, and how Propel(x) helped along the way.
Propel(x): In your own words, tell us what TranscribeMe does.
TranscribeMe!: TranscribeMe converts voice-to-text with very high accuracy at a very low cost. Our customers are typically enterprise users such as academic institutions, media companies, legal and medical organizations, and contact centers. As a result, our customers have large quantities of audio and video content that they need converted into text.
We do this by operating a platform that combines artificial intelligence speech recognition algorithms with real human, crowd-sourced transcribers. And we’ve got a very large pool of crowd workers. Over 300,000 in total. These are people that can type audio-to-text and can correct the output of speech recognition, ultimately resulting in the perfect quality that goes to our customers.
We’ve been able to use the output generated by the human workers to train the artificial intelligence algorithms that power our system. In other words, we can continuously improve the quality of speech recognition technology by giving and verifying examples. And that’s the deep learning type application in our model. We do a lot of machine learning, and this is done by having humans who provide output and training for the computers.
Propel(x): Could you tell me a little bit about the size of your company? How many people do you have on your management team?
TranscribeMe!: Overall in the company we’ve got about 25 people. There’s five people on the management team, so we’re quite a small firm. We’re a startup in every sense of the word.
Propel(x): Can you explain to me how this technology is a leap vis-a-vis the current state of the industry?
TranscribeMe!: It’s interesting to think about it, because most other things working on artificial intelligence typically focus on the algorithm component rather than the training component. The training component, as well as the models that are generated by using student verified data, provides the largest boost in the output quality. And so, we’re very different in the sense that we’ve actually built our entire worker infrastructure for our crowd engagement. We can have hundreds of thousands of people contribute, which works towards training artificial intelligence.
Propel(x): I understand that you recently closed a round. Could you walk us through what your funding process was like?
TranscribeMe!: So far, we have funded the company with support from angel investors. And by and large, angel investors in California. For this round we had key funding from Keiretsu Forum, from The Angel’s Forum, Keiretsu San Francisco, Pasadena Angels, and Tech Coast Angels in Los Angeles.
Propel(x): Did you find that there were any particular challenges in your fundraising because you are in the deep technology industry?
TranscribeMe!: There are always challenges with fundraising. It’s never straightforward. If you look for challenges that are associated with being in the deep-learning space, I would probably come in the other way. I would say in the last six months, there’s been increased investor awareness of machine-learning and AI. Especially in comparison to earlier on, where most investors were not interested in the space. There’s now a lot of awareness and a lot of interest.
Propel(x): Why did you decide to fundraise with Propel(x)?
TranscribeMe!: We had a relationship with the founders and have seen Propel(x) grow from its early stages. We’ve seen Propel(x) engage with similar investors as those working with TranscribeMe, so there was a good opportunity there. More importantly, it allowed us access to not only investors, but to experts in the speech recognition and machine learning space. I was particularly impressed with the caliber of experts that Propel(x) brought to the table to look at our company and provide feedback and advice. I thought that was very valuable.
Propel(x): Do you have any advice for other entrepreneurs just starting out?
TranscribeMe!: In terms of fundraising, I think it’s important to understand that fundraising is very much a process. You have to think about how best to apply for screenings and how to get through the screening committee, how to prepare your message for a group of interested investors and then how to close. It is a process that requires a lot of attention. This is something that can very well take up a full time engagement from entrepreneurs, and people need to be aware that there are no shortcuts. It requires a lot of work and understanding.
Propel(x): So, 10 years down the line, TranscribeMe is a huge success. How has society been impacted?
TranscribeMe!: Very positively, I hope. Our goal is to help computers understand the human voice, not just very short commands. This will allow people to interact in the most natural way possible with computers. This is a very ambitious task that I hope impacts society positively. It certainly opens up a lot of avenues for technologies that are yet to come.
It’s important for computers to be able to understand natural human voice, so that we don’t have to modulate what we say, where we can speak with an accent, where we can truly express ourselves in the manner that’s apparent to even an infant. Voice expression is a fundamental part of human expression, and having computers understand human voice is very important.