Title
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization.
Abstract
We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a graph induced by the data. The main contribution of this work is a computationally efficient, stochastic graph-regularization technique that uses mini-batches that are consistent with the graph structure, but also provides enough stochasticity (in terms of mini-batch data diversity) for 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.
Year
Venue
Field
2016
arXiv: Machine Learning
Convergence (routing),Speech corpus,TIMIT,Stochastic gradient descent,Data set,Pattern recognition,Phone,Regularization (mathematics),Artificial intelligence,Machine learning,Mathematics,Scalability
DocType
Volume
Citations 
Journal
abs/1612.04899
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Sunil Thulasidasan112712.51
Jeff A. Bilmes227816.88