By Topic

Mutual information neural networks: a new connectionist approach for dynamic speech recognition tasks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Rigoll, G. ; Dept. of Comput. Sci., Duisburg Univ., Germany

A new probabilistic neural network paradigm for dynamic pattern recognition problems is presented. The approach includes the following innovations: 1) it is based on a self-organizing learning approach using information theory principles. 2) The neuron activations are interpreted as probabilities and represent probabilistic decision boundaries in the feature space. 3) A combination of unsupervised and supervised learning algorithms is used to train the network weights. 4) The neuron probabilities can be further refined by corrective training methods. 5) The neural network can process dynamic patterns of arbitrary length, and can be even used for continuous speech recognition, although it is not a recurrent network. 6) The output activations of the neural network can be evaluated directly or optionally treated as input to hidden Markov models in order to construct a hybrid recognition system. The network has been tested for the recognition of dynamic speech patterns and performs better than a discrete HMM system with a codebook size equal to the number of output neurons in the neural net

Published in:

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on  (Volume:ii )

Date of Conference:

19-22 Apr 1994