Deep autoencoders augmented with phone-class feature for reverberant speech recognition

Abstract

This paper addresses reverberant speech recognition based on front-end processing using DAE (Deep AutoEncoder) coupled with DNN (Deep Neural Network) acoustic model. DAE can effectively and flexibly learn mapping from corrupted speech to the original clean speech based on the deep learning scheme. While this mapping is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. In the evaluation on the Reverb Challenge 2014 task, the augmented feature in either type results in a significant improvement (7–8% relative) from the standard DAE. It is also shown that using the soft representation in the training phase is critical.