Sciweavers

Share
IJCNN
2006
IEEE

Reservoir-based techniques for speech recognition

9 years 9 months ago
Reservoir-based techniques for speech recognition
— A solution for the slow convergence of most learning rules for Recurrent Neural Networks (RNN) has been proposed under the terms Liquid State Machines (LSM) and Echo State Networks (ESN). These methods use a RNN as a reservoir that is not trained. For this article we build upon previous work, where we used reservoir-based techniques to solve the task of isolated digit recognition. We present a straightforward improvement of our previous LSM-based implementation that results in an outperformance of a stateof-the-art Hidden Markov Model (HMM) based recognizer. Also, we apply the Echo State approach to the problem, which allows us to investigate the impact of several interconnection parameters on the performance of our speech recognizer.
David Verstraeten, Benjamin Schrauwen, Dirk Stroob
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors David Verstraeten, Benjamin Schrauwen, Dirk Stroobandt
Comments (0)
books