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An Application of Recurrent Nets to Phone Probability Estimation
 IEEE Transactions on Neural Networks
, 1994
"... This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed ..."
Abstract

Cited by 207 (8 self)
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This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed
unknown title
"... This chapter was written in 1994. Further advances have been made such as: context dependent phone modelling; forwardbackward training and adaptation using linear input transformations. This chapter describes a use of recurrent neural networks (i.e., feedback is incorpo rated in the computation) ..."
Abstract
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This chapter was written in 1994. Further advances have been made such as: context dependent phone modelling; forwardbackward training and adaptation using linear input transformations. This chapter describes a use of recurrent neural networks (i.e., feedback is incorpo rated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate pa rameter estimation procedure. For each frame of acoustic data, the recurrent network generates an estimate of the posterior probability of of the possible phones given the observed acoustic signal. The posteriors are then converted into scaled likelihoods and used as the observation probabilities within a conventional decoding paradigm (e.g., Viterbi decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative to conventional, largevocabulary, HMM systems). Most if not all automatic speech recognition systems explicitly or implicitly compute a (equivalently, etc.) indicating how well an input acoustic signal matches a speech model of the hypothesised utterance. A fundamental problem in speech recognition is how this score may be computed,