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Links Between Markov Models and Multilayer Perceptrons
December 1990 (vol. 12 no. 12)
pp. 1167-1178

The statistical use of a particular classic form of a connectionist system, the multilayer perceptron (MLP), is described in the context of the recognition of continuous speech. A discriminant hidden Markov model (HMM) is defined, and it is shown how a particular MLP with contextual and extra feedback input units can be considered as a general form of such a Markov model. A link between these discriminant HMMs, trained along the Viterbi algorithm, and any other approach based on least mean square minimization of an error function (LMSE) is established. It is shown theoretically and experimentally that the outputs of the MLP (when trained along the LMSE or the entropy criterion) approximate the probability distribution over output classes conditioned on the input, i.e. the maximum a posteriori probabilities. Results of a series of speech recognition experiments are reported. The possibility of embedding MLP into HMM is described. Relations with other recurrent networks are also explained.

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Index Terms:
speech recognition; Markov models; multilayer perceptrons; connectionist system; discriminant hidden Markov model; Viterbi algorithm; least mean square minimization; error function; probability; Markov processes; minimisation; neural nets; probability; speech recognition
Citation:
H. Bourlard, C.J. Wellekens, "Links Between Markov Models and Multilayer Perceptrons," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 12, pp. 1167-1178, Dec. 1990, doi:10.1109/34.62605
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