Results 1 
3 of
3
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 193 (8 self)
 Add to MetaCart
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
PolicyGradient Algorithms for Partially Observable Markov decision processes
, 2003
"... Partially observable Markov decision processes are interesting because of their ability to model most conceivable realworld learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms ..."
Abstract

Cited by 25 (2 self)
 Add to MetaCart
Partially observable Markov decision processes are interesting because of their ability to model most conceivable realworld learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms are computationally intractable. Such computational complexity motivates approximate approaches. One such class of algorithms are the socalled policygradient methods from reinforcement learning. They seek to adjust the parameters of an agent in the direction that maximises the longterm average of a reward signal. Policygradient methods are attractive as a scalable approach for controlling partially observable Markov decision processes (POMDPs). In the most
7 THE USE OF RECURRENT NEURAL NETWORKS IN CONTINUOUS SPEECH RECOGNITION
"... This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate parameter estimation procedure. For each frame of acou ..."
Abstract
 Add to MetaCart
This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate parameter 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 3 to conventional, largevocabulary, HMM systems).