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An Application of Recurrent Nets to Phone Probability Estimation (1994) [153 citations — 8 self]

by Tony Robinson
IEEE Transactions on Neural Networks
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Abstract:

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

Citations

2372 A tutorial on hidden Markov Models and selected applications in speech recognition – Rabiner - 1989
2044 Learning internal representations by error propagation – Rumelhart, G, et al. - 1986
1140 Finding structure in time – Elman - 1990
325 Connectionist speech recognition : a hybrid approach – Bourlard, Morgan - 1994
321 A learning Algorithm for Continually Running Fully Recurrent Neural Networks – Williams, Zipser - 1989
302 A maximum likelihood approach to continuous speech recognition – Bahl, Jelinek, et al. - 1983
274 Connectionist learning procedure – Hinton - 1989
273 Phoneme recognition using time-delay neural networks – Waibel, Hanazawa, et al. - 1989
255 Increased Rates Of Convergence Through Learning Rate Adaptation – Jacobs - 1988
230 Interpolated estimation of Markov source parameters from sparse data – Jelinek, Mercer - 1980
204 Neural Network Classifiers Estimate Bayesian a posteriori Probabilities – Richard, Lippmann - 1991
203 Hidden Markov Models for speech recognition – Huang, Ariki - 1990
182 Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition – Bridle - 1989
172 Backpropagation through time: What it does and how to do it – Werbos - 1990
165 Automatic Speech Recognition: The Development of the SPHINX – Lee - 1989
136 Speaker-independent phone recognition using hidden Markov models – Lee, Hon - 1989
109 Speaker-Independent Isolated Word Recognition Using Dynamic Features of Speech Spectrum – Furui - 1986
99 A course in phonetics – Ladefoged - 1975
92 Speech Database Development: Design and Analysis of the Acoustic–Phonetic Corpus – Lamel, Kassel, et al. - 1987
90 Links between Markov models and multilayer perceptrons – Bourland, Wellekens - 1990
86 Gradient-based learning algorithms for recurrent networks and their computational complexity – Williams, Zipser - 1994
79 The DARPA 1000-word resource management database for continuous speech recognition – Price, Fisher, et al. - 1988
53 Optimization of the backpropagation algorithm for training multilayer perceptrons – Schiffmann, Joost, et al. - 1993
49 Linear Discriminant Analysis for Improved Large Vocabulary Speech Recognition – Haeb-Umbach, Ney - 1992
46 Learning complex, extended sequences using the principle of history compression – Schmidhuber - 1992
45 Phonological Structures for Speech recognition – Cohen - 1989
44 A probabilistic approach to the understanding and training of neural network classifiers – Gish - 1990
43 Connectionist probability estimators in HMM speech recognition – Renals, Morgan, et al. - 1993
42 Continuous speech recognition using multilayer perceptrons with hidden Markov models – Morgan, Bourlard - 1990
39 ªSupervised Learning of Probability Distributions by Neural Networks,º Neural Information Processing Systems – Baum, Wilczek - 1988
31 Static and dynamic error propagation networks with application to speech coding – Robinson, Fallside - 1988
31 F.Fallside, "A Recurrent Error Propagation Network Speech Recognizer System – Robinson - 1991
27 MMI training for continuous phoneme recognition on the TIMIT database – Kapadia, Valtchev, et al. - 1993
26 The HTK tied-state continuous speech recogniser – Woodland, Young - 1993
25 Alphanets: a recurrent `neural' network architecture with a hidden markov model interpretation – Bridle - 1990
20 Dynamic recurrent neural networks – Pearlmutter - 1990
18 Combining hidden markov models and neural network classifiers – Niles, Silverman - 1990
16 Connectionist Probability Estimation in the Decipher Speech Recognition System – Renals, Morgan, et al. - 1992
16 CDNN: A Context Dependent Neural Network for Continuous Speech Recognition – Bourlard, Morgan, et al. - 1992
15 A connectionist model for phoneme recognition in continuous speech – Harrison, Fallside - 1989
12 Several Improvements to a Recurrent Error Propagation Network Phone Recognition System – Robinson - 1991
12 Fast algorithms for phone classification and recognition using segment-based models – Digalakis, Ostendorf, et al. - 1992
10 rst look at phonetic discrimination using connectionist models with recurrent links – Kuhn, \A - 1987
9 Context-dependent multiple distribution phonetic modeling with MLPs – Cohen, Franco, et al. - 1993
8 An Alphanet approach to optimising input transformations for continuous speech recognition – Bridle, Dodd - 1991
7 Competitive training in hidden Markov models – Young - 1990
7 Soft weight-sharing – Nowlan, Hinton - 1992
6 A Comparison of Preprocessors for the Cambridge Recurrent Error Propagation Network Speech System – Robinson, Holdsworth, et al. - 1990
2 Practical network design and implementation – Robinson - 1992
2 The state space and "ideal input" representations of recurrent networks – Robinson - 1992