425 citations · 559 across the 29 of their papers we have counts for
6 papers · 1 filter
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat, Yossi Adi, Alexander Schwing +1
Generalization bounds which assess the difference between the true risk and the empirical risk, have been studied extensively. However, to obtain bounds, current techniques use str…
Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling
Itai Gat, Felix Kreuk, Tu Anh Nguyen +5
Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LM…
A Functional Information Perspective on Model Interpretation
Itai Gat, Nitay Calderon, Roi Reichart +1
Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for mo…
Towards a Common Speech Analysis Engine
Hagai Aronowitz, Itai Gat, Edmilson Morais +2
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-super…
Speech Emotion Recognition using Self-Supervised Features
Edmilson Morais, Ron Hoory, Weizhong Zhu +3
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speec…
Speaker Normalization for Self-supervised Speech Emotion Recognition
Itai Gat, Hagai Aronowitz, Weizhong Zhu +2
Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases. Deep-net-based classifiers, in turn, are prone to exploit those biases and find…