Rank based Decoding for Improved DNN/HMM Hybrid Acoustic Models in the EML Transcription Platform

In this paper we review the use of neural network based acoustic models in the EML Transcription Platform and describe some recent improvements to DNN/HMM hybrid acoustic modeling, including vocal tract length perturbation (VTLP) and the use of a DNN state prior for decoding. We investigate network adaptation techniques to overcome a noise level mismatch between training and test data and propose the use of a robust, rank based decoding method as an alternative to the standard softmax output layer. We examine its impact on word error rate for both original and adapted DNN/HMM models for several noise conditions. Initial results obtained on a publicly available data set suggest that a rank based output layer can outperform the conventional softmax layer and is a powerful method if no adaptation data is available.