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.
Hiding speech inside of speech.
2019 December |
Hide and Speak: Deep Neural Networks for Speech Steganography.
Preprint. |
2018 December |
Deceiving end-to-end deep learning malware detectors using adversarial examples.
Workshop on Security in Machine Learning (NIPS). |
2018 April |
Fooling end-to-end speaker verification with adversarial examples.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). |