Isolated word recognition with the Liquid State Machine: a case study

The Liquid State Machine (LSM) is a recently developed computational model with interesting properties. It can be used for pattern classification, function approximation and other complex tasks. Contrary to most common computational models, the LSM does not require information to be stored in some s...

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Bibliographic Details
Published in:Information processing letters 2005-09, Vol.95 (6), p.521-528
Main Authors: Verstraeten, D., Schrauwen, B., Stroobandt, D., Van Campenhout, J.
Format: Article
Language:English
Subjects:
Case studies
Liquid State Machine
Neural networks
Parallel processing
Speech recognition
Spiking neural networks
Studies
Voice recognition
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Summary:The Liquid State Machine (LSM) is a recently developed computational model with interesting properties. It can be used for pattern classification, function approximation and other complex tasks. Contrary to most common computational models, the LSM does not require information to be stored in some stable state of the system: the inherent dynamics of the system are used by a memoryless readout function to compute the output. In this paper we present a case study of the performance of the Liquid State Machine based on a recurrent spiking neural network by applying it to a well known and well studied problem: speech recognition of isolated digits. We evaluate different ways of coding the speech into spike trains. In its optimal configuration, the performance of the LSM approximates that of a state-of-the-art recognition system. Another interesting conclusion is the fact that the biologically most realistic encoding performs far better than more conventional methods.
ISSN:0020-0190
1872-6119
DOI:10.1016/j.ipl.2005.05.019