Speechmatics, a Cambridge-based startup applying machine learning to automatic speech recognition (ASR), has raised a £6.35 million Series A round, led by AlbionVC with support from IQ Capital and several angel investors.
Awarded a Queen’s Award for Enterprise in ‘Innovation’ earlier this year, the Speechmatics technology stands out for its accuracy, wide variety of languages, and flexible application. The ASR engine is currently available in 29 languages, either in the cloud or on-premises — a critical feature for businesses who do not wish or are unable to share data in the cloud. Applications include nearly instant transcription of audio files, live subtitling in broadcast, and the conversion of call centre recordings into text.
The new funding will enable the company to expand and increase their footprint in the rapidly-growing ASR market, currently worth $7.5 billion and projected to be $21.5 billion by 2024.
Specifically, the company will open new offices in Brno, Czech Republic; Chennai, India; and Denver, USA. The team is expected to double from 50 to 100 people by the end of 2019.
John Milliken, who was appointed as Speechmatics CEO in January to lead such global expansion, says: “We’ve invested heavily into our product and machine learning capabilities to provide our customers with what we believe is the best speech recognition technology on the markets. We recognise that businesses are increasingly aware of the importance of unlocking their voice data and utilising it for core business functions such as compliance, analytics, training and improving customer experience. This latest investment opens up not only global expansion but the opportunity for new product development and traction in new markets.”
Ed Stacey, Managing Partner at IQ Capital, says: "We have supported Speechmatics since IQ Capital first invested in 2016. All companies in our portfolio have the potential for global scale, and we believe Speechmatics, with over 75 languages in development, is set to become a global market leader in speech recognition and in allowing machines to understand natural languages.”