post NIPS workshop on speech

[Uli Bodenhausen]

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Optimization of Neural Network Architectures for Speech Recognition
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	Dec. 7, 1991, Vail, Colorado

	Uli Bodenhausen, Universitaet Karlsruhe
	Alex Waibel, Carnegie Mellon University

A variety of neural network algorithms have recently been applied to
speech recognition tasks. Besides having learning algorithms for
weights, optimization of the network architectures is required to
achieve good performance. Also of critical importance is the
optimization of neural network architectures within hybrid systems
for best performance of the system as a whole. 

Parameters that have to be optimized within these constraints include 

	the number of hidden units, 
	number of hidden layers, 
	time-delays, 
	connectivity within the network, 
	input windows, 
	the number of network modules, 
	number of states

and others. The workshop intends to discuss and evaluate the
importance of these architectural parameters and different integration
strategies for speech recognition systems. 

Participants are welcome to present short case studies on the optimization 
of neural networks, preferably with an evaluation of the optimization steps. 
It would also be nice to hear about some rather unconventional techniques
of optimization (as long as its not vodoo or the 'shake the disk during 
compilation' type of technique).

The workshop could also be of interest to researchers working on 
constructive/destructive learning algorithms because the relevance of 
different architectural parameters should be considered for the design 
of these algorithms.

The following speakers have already confirmed their participation:
	Kenichi Iso, NEC Corporation, Japan
	Patrich Haffner, CNET, France
	Mike Franzini, Telefonica I + D, Spain
	Allen Gorin, AT&T, USA
	Yoshua Bengio, MIT

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Further contributions are welcome. Please send mail to uli at ira.uka.de or
uli at speech2.cs.cmu.edu.
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