Building an ensemble of CD-DNN-HMM acoustic model using random forests of phonetic decision trees
Tuo Zhao, Yunxin Zhao, Xin Chen · 2014
We propose an RF-PDT+CD-DNN approach to generate an ensemble of context-dependent pre-trained deep neural networks (CD-DNNs) using random forests of phonetic decision trees (RF-PDTs) and constructing a CD-DNN-HMM-based ensemble acoustic model (EAM). We present evaluation results on the TIMIT dataset and a telemedicine automatic captioning dataset and demonstrate that the proposed RF-PDT+CD-DNN based EAM significantly outperforms the CD-DNN based single acoustic model (SAM) in phone and word recognition accuracies.