Deep Belief Networks for phone recognition

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 p r cess. Deep Belief Networks (DBNs) have recently proved to be very effective fo r a variety of machine learning problems and this paper applies DBNs to acous ti modeling. On the standard TIMIT corpus, DBNs consistently outperform ot her techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the T IMIT core test set.

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