Speech Recognition with Dynamic Bayesian Networks (1998) [81 citations — 9 self]
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@TECHREPORT{Zweig98speechrecognition,author = {Geoffrey Zweig and Stuart Russell},
title = {Speech Recognition with Dynamic Bayesian Networks},
institution = {},
year = {1998}
}
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Abstract:
Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech recognition. The factored state representation enabled by DBNs allows us to explicitly represent long-term articulatory and acoustic context in addition to the phonetic-state information maintained by hidden Markov models (HMMs). Furthermore, it enables us to model the short-term correlations among multiple observation streams within single time-frames. Given a DBN structure capable of representing these long- and short-term correlations, we applied the EM algorithm to learn models with up to 500,000 parameters. The use of structured DBN models decreased the error rate by 12 to 29 % on a large-vocabulary isolated-word recognition task, compared to a discrete HMM; it also improved significantly on other published results for the same task. This is the first successful application of DBNs to a largescale speech recognition problem. Investigation of the learned models indicates that the hidden state variables are strongly correlated with acoustic properties of the speech signal.
