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Acoustic modeling with neural graph embeddings

Graph-based learning (GBL) is a form of semi-supervised learning that has been successfully exploited in acoustic modeling in the past. It utilizes manifold information in speech data that is represented as a joint similarity graph over training and test samples. Typically, GBL is used at the output level of an acoustic classifier; however, this setup is difficult to scale to large data sets, and the graph-based learner is not optimized jointly with other components of the speech recognition system. In this paper we explore a different approach where the similarity graph is first embedded into continuous space using a neural autoencoder. Features derived from this encoding are then used at the input level to a standard DNN-based speech recognizer. We demonstrate improved scalability and performance compared to the standard GBL approach as well as significant improvements in word error rate on a medium-vocabulary Switchboard task.