Workshop on Machine Learning in Speech and Language Processing
August 11, 2017
Sydney, Australia
Speaker: Tasha Nagamine (Columbia)
Title: Feature Representation and Transformation in Multilayer Perceptron Acoustic Models
Abstract:
While deep learning has shown great success in recent years, how the nodes
in different layers of neural networks represent both the input and the
properties of the network function remain unknown. We present an empirical
and joint framework to study the encoding properties of node activations in
hidden and output layers of the network, and to construct the equivalent
linear transformation applied to each data point. These methods are used to
discern and quantify the properties of feed-forward neural networks trained
to map acoustic features to phoneme labels. We show a selective and
progressively nonlinear warping of the feature space in which the most
discriminant dimensions of the input samples are emphasized. Analyzing the
sample-dependent linear transforms applied to each data sample shows that
categorization is achieved by forming prototypical templates to explicitly
model the all the variations of each class. This work provides a
comprehensive analysis of representation and computation of neural networks
and provides an intuitive account of how deep neural networks perform
classification.