In this paper, we analyze the properties of invertible neural networks, which provide a way solving inverse problems. Our main focus lies on investigating and controlling Lipschitz constants corresponding networks. Without such an control, numerical simulations are prone to errors not much is gained against traditional approaches. Fortunately, our analysis indicates that changing latent distrib...

This paper describes a new approach to acoustic mod-eling for large vocabulary continuous speech recognition (LVCSR) systems. Each phone is modeled with a large Gaussian mixture model (GMM) whose context-dependent mixture weights are estimated with a sentence-level discrim-inative training criterion. The estimation problem is casted in a neural network framework, which enables the incorporation...