Abstract

We propose a rate-distortion based deep neural network (DNN) training algorithm using a smooth matrix functional on the manifold of positive semi-definite matrices as the non-parametric entropy estimator. The objective in the optimization function includes not only the measure of performance of the output layer but also the measure of information distortion between consecutive layers in order to produce a concise representation of its input on each layer. An experiment on speech emotion recognition shows the DNN trained by such method reaches comparable performance with an encoder-decoder system.

Date

2016

Authors

Che-Wei Huang, Shrikanth Shri S Narayanan

Conference

2016 IEEE 26th international workshop on machine learning for signal processing (MLSP)

Pages

1-6

Publisher

IEEE