Acoustic classification using semi-supervised Deep Neural Networks and stochastic entropy-regularization over nearest-neighbor graphs

Abstract

We describe a graph-based semi-supervised learning method for acoustic data that uses a Deep Neural Network (DNN) combined with a stochastic graph-based entropic regularizer to favor smooth solutions over a graph induced by the data. We consider graph embeddings constructed from the input features and also from dimensionality-reduced encodings obtained from the bottleneck layer of a separate deep auto-encoder. We use a computationally efficient, stochastic graph-regularization technique that uses mini-batches that are consistent with the graph structure but that also provide enough data diversity for the convergence of stochastic gradient descent methods to good solutions. For this work, we focus on results of frame-level phone classification accuracy on the TIMIT speech corpus but our method is general and scalable to much larger data sets. Results indicate that our method significantly improves classification accuracy compared to the fully-supervised case when the fraction of labeled data is low, and it is competitive with other methods in the fully labeled case.