Showing papers by "Yoshua Bengio published in 1992"
TL;DR: In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM, and an algorithm is proposed for global optimization of all the parameters.
Abstract: The integration of multilayered and recurrent artificial neural networks (ANNs) with hidden Markov models (HMMs) is addressed. ANNs are suitable for approximating functions that compute new acoustic parameters, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters. Results on speaker-independent recognition experiments using this integrated ANN-HMM system on the TIMIT continuous speech database are reported. >
234 citations
TL;DR: A novel learning algorithm is proposed, called Back-Propagation for Sequences (BPS), for a particular class of dynamic neural networks in which some units have local feedback, and it has the same time complexity and space requirements as back-propagation (BP) applied to feedforward networks.
39 citations
01 Jan 1992
TL;DR: Results of several experiments with these networks on the recognition of phonemes for the TIMIT database are presented, including an experiment on a recurrent network of RBFs.
Abstract: the purpose of this paper is to study the application of Radial Basis Functions (RBF) to automatic speech recognition. Results of several experiments with these networks on the recognition of phonemes for the TIMIT database are presented, including an experiment on a recurrent network of RBFs.
2 citations