Evolving From Speech Recognition to Speech Understanding

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in Blogs
19 July 2016

Just a few years ago, speech recognition was a huge challenge, and one of the most coveted hands-free user interfaces. I can easily recall how the first versions of Siri misheard what I said, or repeatedly responded “Sorry, I didn’t quite get that”. Today, significant advances in machine learning, spurred by cheaper and more efficient processing power, have made speech recognition so ubiquitous, that it’s practically taken for granted. In a recent keynote, Google Senior Fellow Jeff Dean claimed that neural networks reduced word errors by 30% when applied to speech recognition. Alongside direct improvements in speech recognition, noise cancellation and speech enhancement has also benefitted significantly from neural networks. An excellent example of this is Cypher’s technology, which isolates voice using deep neural networks. ASR targeted noise cancellation improves the raw data for the speech recognition engine, making the task more likely to succeed. These factors have led to the current state, in which speech recognition is a reliable, useful interface on many devices.

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Moshe Sheier

Director of Strategic Marketing, CEVA