Automatic speech recognition using a predictive echo state network classifier.
We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.
Skowronski
Mark D
MD
Computational NeuroEngineering Lab, NEB 465, University of Florida, Gainesville, FL 32611, USA. markskow@cnel.ufl.edu
Harris
John G
JG
D000704:Analysis of Variance; D001185:Artificial Intelligence; D006801:Humans; D008390:Markov Chains; D009622:Noise; D010363:Pattern Recognition, Automated; D021641:Recognition, Psychology / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013060:Speech / Q000502:physiology*; D013067:Speech Perception / Q000502:physiology*; D049250:Speech Recognition Software