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Automated transcription provider Verbit raises $23M


Verbit Software Ltd., a provider of automated video and speech transcription services powered by artificial intelligence, is hoping to take on giants such as Google LLC after closing on a new $23 million round of funding led by Viola Ventures.

Vertex Ventures, HV Ventures, Oryzn Capital, Vintage Venture Partners and Clal-Tech also participated in the Series A round, which brings Verbit’s total funding raised to $34 million following an earlier $11 million seed round.

Verbit is fighting for business in an extremely competitive transcription space that’s growing in importance to enterprises for obvious reasons because it saves them huge amounts of time and money. Verbit’s problem is that it faces some serious competitors in Google, Microsoft Corp. and Amazon Web Services Inc., all of which have developed their own automated video and audio transcription capabilities in recent years.

Going up against established competitors of that nature is no easy task, but Verbit offers a key differentiator in that it combines its AI speech recognition software with an on-demand network of human transcribers in order to boost the accuracy of its services. The company makes some bold claims, saying it can transcribe video and audio with 99 percent accuracy when human transcribers are involved.

“Our solution involves an extensive network of freelancers, who edit and review the transcripts that are generated by our AI technology,” Tom Livne, Verbit’s chief executive officer, told SiliconANGLE. “This hybrid approach enables Verbit’s customers to optimize their workflows and meet the increasing demand for transcription services, which is reflected in the company’s high customer retention and renewal rates.”

When Verbit’s speech-to-text technology works alone, it’s able to transcribe one hour of speech in just five minutes, Livne said. But the transcriptions may be less accurate with software alone.

Verbit’s software also allows data in speeches to be analyzed for potential insights. This is because it incorporates natural language processing technology, supported by adapted speech recognition models, Livne added.

“Natural language process querying provides the ability to obtain complete insights on all the data in context, not just isolated segments of it,” Livne explained. “It also helps uncover patterns and trends in the vast amount of data, making it possible to conduct more complex analyses.”

The AI/human combo seems to have some appeal in niche industries, with Verbit claiming more than 100 customers in legal and higher education alone. Its services are said to be very popular in the legal industry to help fill the court reporter talent shortage, for example.

“Transcription is a good example where progress in AI and cheap compute services from the cloud enable a more automated approach,” said Holger Mueller, principal analyst and vice president of Constellation Research Inc. “It helps reduce human error rates and comes with 24/7 availability. That’s the market Verbit is after.”

Fresh with new funding, Verbit says its main aim now is to accelerate its expansion in the U.S. The plan is to grow its sales, marketing and product teams, and funnel more money into the development of new capabilities for its transcription platform.

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Automated Transcription Provider Verbit Raises USD 23m in Series A

Tel Aviv-based transcription platform Verbit raised USD 23m in a Series A round led by Viola Ventures toward the end of January 2019. HV Ventures, Oryzn Capital, Vintage Venture Partners, and ClalTech also participated in the round, which brings Verbit’s total funding raised to USD 34m.

Verbit offers video and audio transcription services primarily through AI-powered automated speech-to-text technology refined through a network of human transcribers and post-editors.

In an article on the funding round, company CEO Tom Livne claimed the platform can transcribe an hour of audio in five minutes. He also said the vast amounts of transcribed text can be analyzed for patterns and insights through natural language processing (NLP) technology, noting that Verbit’s model has attracted “more than 100 customers in legal and higher education.”

Slator reached out to Livne for more information on the company and its latest funding round.

Court Reporter Shortage

Livne declined to comment on the company’s valuation but shared a few other interesting details including some of the most common client use cases.

In the education sector, Livne said Verbit fulfills the requirement for higher education institutions to provide captioned videos and full academic transcription for deaf or hearing-impaired students. He said some education sector clients include the London Business School, Stanford, Harvard, FIT, Brigham Young University-Idaho, and the University of California, Santa Barbara, as well as online course providers Coursera and Udacity.

“In the legal industry, Verbit’s solution is able to help tackle the court reporter shortage and provide companies with the technology to make reporters more efficient to cover all jobs,” Livne said.

Livne appears to have come full circle from his original inspiration for the company. “Before I found myself in the world of tech startups, I began my career in law, where transcripts are essential. I was often unsatisfied with the slow turnaround time for a finished transcript,” Livne said, adding, “Drawing on my prior experience with legal transcription, I felt that this was an important need in the market and that a viable solution could be found with the right technology.”

In the same article on the latest funding round, Livne explained that the money will go into accelerating expansion in the US as well as developing new capabilities for the platform.

For now, Verbit is mostly confined to the US as its key geographic market and region with the most clients. The platform currently supports English and Spanish, Livne said, adding that they intend to “expand to support other languages according to the market demand.”

Transcription vis-à-vis Translation

Transcription shares a number of similarities to translation in terms of the supply chain and, now, the underlying NLP technology. In fact, companies like France-based Ubiqus employ tech stacks that enable them to develop their automatic transcription and translation solutions at the same time.

If Slator’s own experience with a different AI-supported transcription platform is any indication, the technology has developed far enough to be quite helpful for very clear audio files; although it still struggles with background noise, accents, and differentiating speakers. This parallels developments in neural machine translation: now more useful than ever albeit with limitations.

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