Hynek Hermansky receives 2020 IEEE James L. Flanagan Speech and Audio Processing Award

July 5, 2019

Hynek Hermansky

Hynek Hermansky, the Julian S. Smith Professor in the Department of Electrical and Computer Engineering and director of the Center for Language and Speech Processing (CLSP), has been named the recipient of the 2020 IEEE James L. Flanagan Speech and Audio Processing Award.

Administered through IEEE’s Technical Field Council, this award is given annually in recognition of outstanding contributions to the advancement of speech and/or audio signal processing. Hermansky was tapped for his pioneering “contributions to speech processing and feature extraction for robust speech recognition.”

Honored to receive the award, Hermansky has focused on pushing his research “to its limits” for more than three decades.

“Communication by speech is one of the most significant achievements of humankind. Taking advantage of current machine’s abilities to quickly process large amounts of data is certainly useful in this endeavor, but equally and perhaps even more useful is to better understand the relevant aspects of human speech communication,” said Hermansky, who came to JHU in 2008. “I think that what drives my research is the challenge of proving myself in doing what has not yet been done.”

Though his formal training is in engineering, Hermansky has always been interested in cognitive science, psychology and neuroscience. At the moment, he is trying to understand how speech evolved so that its structure reflects properties of human hearing and how the human listener does select optimal strategies for speech processing under changing conditions. He believes knowing this could help in developing more advanced engineering systems for the processing of speech.

As a human language and speech researcher, he finds Johns Hopkins an “excellent place to be” because of the access, it provides to top-level colleagues working in both engineering and the life sciences.

“Emulating aspects of human abilities in speech communication in machines reveals weaknesses of our engineering approaches and hopefully keeps us humble in front of the forces of nature,” Hermansky said. “Convincing students and funding agencies about the importance of such understanding rather than focusing on applications only, can be a struggle in this age of fast and stellar successes in machine learning. So far, I am fortunate that Johns Hopkins gives me the space to do what I do.”

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