The research in the lab is focused on statistical and machine learning techniques applied to the modeling and processing of speech and language. A typical problem in speech and language processing has a very large number of training examples, is sequential, highly structured, and has a unique measure of performance. The lab's goal is to develop rigorous statistical and machine learning algorithms that maximize performance by matching the internal structure of the problem and by optimizing its unique measure of performance.
News
Research
Hide and Speak, Deep Neural Networks for Speech Steganography
Hiding speech inside of speech.
People
Faculty
Joseph Keshet
Professor
PhD Students
Felix Kreuk
Speech Generation
Joseph Shrem
Speech Segmentation
Fuchs Tzviya
Speech Segmentation
Yael Segal
Speech Segmentation
Gavi Shalev
XXX
Msc Students
Tamar Fenster
XXX
Gal Lev
XXX
Roni Chern
Robustness
Talia Ben-Simon
Speech Generation
Alumni
Yossi Adi
Publications
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2019 December |
Hide and Speak: Deep Neural Networks for Speech Steganography.
Preprint. |
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2018 December |
Deceiving end-to-end deep learning malware detectors using adversarial examples.
Workshop on Security in Machine Learning (NIPS). |
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2018 April |
Fooling end-to-end speaker verification with adversarial examples.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). |