Noisy Speech Recognition Based on RBF Neural Network
Year: 2011
Type: article
Abstract: A noisy speech recognition method based on improved RBF neural network is presented, which the parameters of hidden layer are trained dynamically, and Akaike’s final prediction error standard (FPE) is employed to simplify the network. Comparing with two other training methods of RBF network, experimental results based on noisy speech samples show that this method achieves excellent performance in terms of recognition rate and recognition speed.
Source: Advanced materials research
Authors: Gang Yan, Hai Dong Kong, Yang Yu, Xiao Xia Zheng
Institution: Hebei University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2035436170