| 1 (2008) | |||||||||||||||||
Abstract | |||||||||||||||||
| This chapter was written in 1994. Further advances have been made such as: context- dependent phone modelling; forward-backward 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, large-vocabulary, 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, | |||||||||||||||||
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