Berrak Sisman is internationally recognized for her contributions to deep learning, speech synthesis, voice conversion, and the study of emotion and expressiveness in speech. She is an assistant professor in the Department of Electrical and Computer Engineering, where she is affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing.
Sisman received her PhD from the National University of Singapore in 2020. During her PhD studies, she was a visiting researcher at the University of Edinburgh (2019) and the Nara Institute of Science and Technology in Japan (2018). Before joining Johns Hopkins, Sisman was a tenure-track faculty member at the University of Texas at Dallas (2022 to 2024), leading the Speech & Machine Learning Laboratory. Sisman is the recipient of multiple awards, grants, and fellowships, including the NSF CAREER Award (2024), Amazon Faculty Research Award (2022), Singapore Ministry of Education Award (2021), and A*STAR Singapore International Graduate Award (2016 to 2020).
Her leadership roles include serving as general coordinator of the Student Advisory Committee and Postdoc Advisory Committee of the International Speech Communication Association, local arrangement co-chair for IEEE ASRU 2019, publication chair for IEEE ICASSP 2022, and the diversity and inclusion chair for IEEE SLT 2024. She has also served as area chair for major conferences such as INTERSPEECH (2021 to 2022 and 2025), IEEE SLT (2022 to 2024), and IEEE ICASSP (2023 to 2025). She has been elected twice as a member of the IEEE Speech and Language Processing Technical Committee in the area of speech synthesis (2022 to 2024 and 2025 to 2027). She is an associate editor for IEEE Transactions on Affective Computing.
At Johns Hopkins, Sisman leads the Speech & Machine Learning Lab, where her team develops state-of-the-art neural models for speech information processing. Her research areas include Artificial Intelligence, Speech Information Processing, Speech Synthesis and Voice Conversion, Speech Emotion, and Secure Speech Technology.