4 papers
PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting
Bronya Roni Chernyak, Yael Segal, Yosi Shrem +1
Deep learning models excel at many tasks but rely on the assumption that training and test data follow the same distribution. This assumption often does not hold in real-world spee…
HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing
Arnon Turetzky, Or Tal, Yael Segal-Feldman +9
We present HebDB, a weakly supervised dataset for spoken language processing in the Hebrew language. HebDB offers roughly 2500 hours of natural and spontaneous speech recordings in…
DeepFry: Identifying Vocal Fry Using Deep Neural Networks
Bronya R. Chernyak, Talia Ben Simon, Yael Segal +4
Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. It occurs in diverse languages and is prevalent in American English, w…
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
Bronya Roni Chernyak, Bhiksha Raj, Tamir Hazan +1
This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy…