## Probability Estimation By Feed-Forward Networks In Continuous Speech Recognition (1991)

Venue: | In Proceedings IEEE Workshop on Neural Networks for Signal Processing |

Citations: | 7 - 3 self |

### BibTeX

@INPROCEEDINGS{Renals91probabilityestimation,

author = {Steve Renals and Nelson Morgan},

title = {Probability Estimation By Feed-Forward Networks In Continuous Speech Recognition},

booktitle = {In Proceedings IEEE Workshop on Neural Networks for Signal Processing},

year = {1991},

pages = {309--318}

}

### OpenURL

### Abstract

We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture density estimators. We show how the neural network training should be modified to resolve this mismatch. We also discuss problems with discriminative training, particularly the problem of dealing with unlabelled training data and the mismatch between model and data priors. L&H Speechproducts, Ieper, B-8900, Belgium ii INTRODUCTION In continuous speech recognition we wish to estimate P(W W 1 jX T 1 , M), the posterior probability of a word sequence W W 1 = w 1 , ..., wW given the acoustic evidence X T 1 = x 1 , ..., x T and the parameters of the models used Q. This probability canno...

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Citation Context ... also be specified. The transition probabilities and the parameters of the output PDFs are frequently estimated using a maximum likelihood training procedure, the forward-backward algorithm (see e.g. =-=[2]-=-). This procedure is optimal if the true model is in the space of models being searched 1 . However, this is not the case for speech recognition. What is desired is not the best possible model of each... |

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