Deep Learning & Speech: Adaptation, the Next Frontier
The speech community is finally excited about deep learning, but we’re proceeding with caution. Adaptation is critical to understanding real-world speech data. We need to adapt to acoustics and language of course, but also to context. To date, DNNs have shown great promise, but their ability to adapt to the unexpected is still in question. This talk will look at where we are today, as well as the challenges still in front of us.
Paul Murphy is one of Clarify's founders and its CEO. Paul's career in software operations industry has spanned twenty years and three continents. Ten years were dedicated to understanding and building large systems on Wall Street for clients like J.P. Morgan and Salomon Brothers. Paul's work in this area allowed him to explore a broad range of computing solutions, from mainframes to web services, and the gamut of space-time tradeoffs required by dissimilar front and back office systems. Thirteen years ago, Paul moved to London to work at Adeptra, a pioneer in the use of automated outbound calling in the area of credit card fraud detection and prevention. As Adeptra's CTO, he developed all of the software which enabled Adeptra to place intelligent interactive outbound calls on behalf of clients. These systems made extensive use of text-to-speech and voice recognition technology. Since then Paul has dedicated his time to developing technologies that leverage emerging voice processing techniques.