Deep Belief Networks for phone recognition
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BibTeX
@MISC{Mohamed_deepbelief,
author = {Abdel-rahman Mohamed and George Dahl and Geoffrey Hinton},
title = {Deep Belief Networks for phone recognition},
year = {}
}
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Abstract
Hidden Markov Models (HMMs) have been the state-of-the-art techniques for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. There are many proposals in the research community for deeper models that are capable of modeling the many types of variability present in the speech generation process. Deep Belief Networks (DBNs) have recently proved to be very effective for a variety of machine learning problems and this paper applies DBNs to acoustic modeling. On the standard TIMIT corpus, DBNs consistently outperform other techniques and the best DBN achieves a phone error rate (PER) of 23.0 % on the TIMIT core test set. 1







