@inproceedings{dahl2011large,
author = {Dahl, George and Yu, Dong and Deng, Li and Acero, Alex},
title = {Large Vocabulary Continuous Speech Recognition With Context-Dependent DBN-HMMS},
booktitle = {Proc. ICASSP, Prague},
year = {2011},
month = {May},
abstract = {The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a context-dependent DBN-HMM system that dramatically outperforms strong Gaussian mixture model (GMM)-HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8% and 9.2% over GMM-HMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0% and 23.2%.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/large-vocabulary-continuous-speech-recognition-with-context-dependent-dbn-hmms/},
edition = {Proc. ICASSP, Prague},
}