Voice Recognition Technology Advances with NSF CAREER Award

Speech rarely occurs under perfect conditions, so useful voice recognition technology needs to adapt. Ambient noise, stress on behalf of the speaker or cultural differences can all affect how well a voice recognition system can accurately convey a speaker’s meaning. Additionally, with increased use of artificial intelligence for voice generation, voice technology needs to support ethical and responsible use, including detecting when a person’s voice has been imitated without permission.

Dr. Berrak Sisman, an assistant professor of electrical and computer engineering from the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas received a 2024 Faculty Early Career Development Program (CAREER) Award from the National Science Foundation (NSF) to advance voice recognition technology research in several areas.

Sisman received a $563,000+ award to study how factors including background noise, emotions and cultural differences affect a voice recognition system’s ability to recognize and accurately render an individual’s speech.

The project also aims to detect speech spoofing, which involves the fraudulent use of speech samples to impersonate a person’s voice, Sisman said.

“The information will be used to create voice recognition technology that can be used for safeguarding ethical and responsible use of voice generation, for detecting and preventing fraud, and to make it more challenging for unauthorized users to mimic speakers,” she said.

Applications for the voice recognition technology research are broad and include security and defense, accessibility and assistive technologies, medical voice preservation, speech therapy and rehabilitation, in addition to entertainment and gaming.

Sisman joined UT Dallas in 2022. Sisman’s laboratory, the Speech & Machine Learning Lab, is part of the Center for Robust Speech Systems, a center with internationally recognized speech researchers housed at UT Dallas.

She earned bachelor’s degrees in electrical and electronics engineering and computer science and engineering as well as a master’s degree in electronics engineering at Isik University in Turkey. She also earned a PhD degree in electrical engineering at the National University of Singapore and was previously a faculty member at Singapore University of Technology and Design where she taught machine learning and deep learning courses.