Name
Title | ||
|---|---|---|
Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees |
Abstract | ||
|---|---|---|
We propose a novel approach to generate an ensemble of context-dependent deep neural networks (CD-DNNs) by using random forests of phonetic decision trees (RF-PDTs) and construct an ensemble acoustic model (EAM) accordingly for speech recognition. We present evaluation results on the TIMIT dataset and a telemedicine automatic captioning dataset and demonstrate the superior performance of the proposed RF-PDT+CD-DNN based EAM over the conventional CD-DNN based single acoustic model (SAM) in phone and word recognition accuracies. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11265-015-1001-9 | Journal of Signal Processing Systems |
Keywords | Field | DocType |
Ensemble acoustic model,Random forest,Phonetic decision tree,Deep neural network,Discriminative pre-training | TIMIT,Decision tree,Closed captioning,Computer science,Phone,Artificial intelligence,Random forest,Pattern recognition,Word recognition,Speech recognition,Hidden Markov model,Machine learning,Acoustic model | Journal |
Volume | Issue | ISSN |
82 | 2 | 1939-8018 |
Citations | PageRank | References |
1 | 0.36 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuo Zhao | 1 | 222 | 40.58 |
| Yunxin Zhao | 2 | 807 | 121.74 |
| Xin Chen | 3 | 116 | 9.64 |