Novel Front-End Features Based on Neural Graph Embeddings for DNN-HMM and LSTM-CTC Acoustic Modeling

In this paper we investigate neural graph embeddings as frontend features for various deep neural network (DNN) architectures for speech recognition. Neural graph embedding features are produced by an autoencoder that maps graph structures defined over speech samples to a continuous vector space. The resulting feature representation is then used to augment the standard acoustic features at the input level of a DNN classifier. We compare two different neural graph embedding methods, one based on a local neighborhood graph encoding, and another based on a global similarity graph encoding. They are evaluated in DNN-HMM-based and LSTM-CTC-based ASR systems on a 110-hour Switchboard conversational speech recognition task. Significant improvements in word error rates are achieved by both methods in the DNN-HMM system, and by global graph embeddings in the LSTM-CTC system.