Speechmatics raises the bar for language accuracy
Machine learning innovation from Cambridge speech technology trailblazer Speechmatics has produced next generation language, with improved accuracy of up to 16 per cent, the company claims.
The technology uptick marks a significant improvement in accuracy across many of its core languages for use in speech-to-text transcription, providing a reliable, scalable and cost-effective solution for partners and customers.
The new development is based on several factors – refinements to the existing technology, deployment of entirely new machine learning algorithms, and extending and enhancing the company’s use of data resources to ensure close alignment with customer needs.
Speechmatics’ internal testing showed an increase in accuracy of up to 16 per cent for Global English and many of its core languages.
The significant accuracy improvement for Global English means Speechmatics will now have one English language model supporting all major accents and dialects.
Removing the need to use multiple languages packs for English dialects means customers will benefit from simplified deployments as well as a reduction in the overall footprint. In turn this reduces the overhead costs for customers regardless of application or use case.
The ‘Next Generation’ languages update is applicable across all use cases, from subtitling news reports, to transcribing meetings, to flagging potential customer issues within a call centre. Speechmatics is committed to providing regular improvements to its core language offering.
David Pye, head of speech at Speechmatics, says: “We’ve analysed the market-leading quantity of data we have on this and, while we may be the first speech technology company to do away with English dialects completely, our expertise has proven that Global English is the right way to drive a shift change in the market and our Next Generation languages update supports this.
“By continuing to innovate and stay ahead of our competition with regards to accuracy, we will remain a leader in this field.
“Our innovation in machine learning means we can make big jumps in advancing speech recognition technology, including dialect-agnostic speech recognition. We’re doing away with specific dialect language models for English as our modelling is now so advanced we no longer need them.”