Programmable execution of multi-layered networks for automatic speech recognition

A set of Multi-Layered Networks allows the integration of information extracted with variable resolution in the time and frequency domains and to keep the number of links between nodes of the networks small for significant generalization during learning with a reasonable training set size.

[1]  Renato De Mori,et al.  Computer Models of Speech Using Fuzzy Algorithms , 1983, Advanced Applications in Pattern Recognition.

[2]  Frederick Jelinek,et al.  The development of an experimental discrete dictation recognizer , 1985, Proceedings of the IEEE.

[3]  Pietro Laface,et al.  Parallel Algorithms for Syllable Recognition in Continuous Speech , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[6]  Renato De Mori,et al.  A continuous parameter and frequency domain based Markov model , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[8]  Renato De Mori,et al.  Learning and Plan Refinement in a Knowledge-Based System for Automatic Speech Recognition , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Geoffrey E. Hinton,et al.  Learning sets of filters using back-propagation , 1987 .

[10]  Jean Rouat,et al.  Use of Procedural Knowledge for Automatic Speech Recognition , 1987, IJCAI.

[11]  Lokendra Shastri,et al.  Learning Phonetic Features Using Connectionist Networks , 1987, IJCAI.

[12]  Lalit R. Bahl,et al.  Speech recognition with continuous-parameter hidden Markov models , 1987, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[13]  Alex Waibel,et al.  Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.