Noisy Speech Recognition Based on RBF Neural Network

2011 ◽  
Vol 271-273 ◽  
pp. 597-602
Author(s):  
Gang Yan ◽  
Hai Dong Kong ◽  
Yang Yu ◽  
Xiao Xia Zheng
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Training Methods ◽  
Noisy Speech ◽  
Rbf Network ◽  
Final Prediction Error ◽  
Noisy Speech Recognition ◽  
Hidden Layer

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.

Noise-Robust Speech Recognition Based on RBF Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 413-418
Author(s):  
Xue Mei Hou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Robust Speech Recognition ◽  
Rbf Network ◽  
Noise Robust Speech Recognition ◽  
Noise Robust

Considering the actuality of current speech recognition and the characteristic of RBF neural network, a noise-robust speech recognition system based on RBF neural network is proposed with the entire-supervised algorithm. If the traditional clustering algorithm is employed, there is a flaw that the node center of hidden layer is always sensitive to the initial value, but if the entire-supervised algorithm is used, the flaw will not turn up, and the classification ability of RBF network will be enhanced. Experimental results show that, compared with the traditional clustering algorithm, the entire-supervised algorithm is of higher recognition rate in different SNRs than that of clustering algorithm.

Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory

2017 ◽  
Vol 8 (2) ◽  
pp. 667 ◽  
Author(s):  
Prakash Ch. Tah ◽  
Anup K. Panda ◽  
Bibhu P. Panigrahi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
New Combination ◽  
Training Methods ◽  
Power Theory ◽  
Rbf Network ◽  
Radial Basis

In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient  training methods called hybrid learning method.The method  requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.

Speech Recognition Algorithm Based on Empirical Mode Decomposition and RBF Neural Network

2013 ◽  
Vol 831 ◽  
pp. 465-469
Author(s):  
Wei Wei Shi ◽  
Wei Hua Xiong ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Empirical Mode Decomposition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Good Method ◽  
Recognition Algorithm ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Learning Speed

This paper presents a novel method of the speech recognition in combining the empirical mode decomposition with radical basis function neural network. Speech signals which pretreated are decomposed by empirical mode decomposition to get a set of intrinsic mode functions. It extracts mel frequency cepstrum coefficient from intrinsic mode function. Features parameters are made up of the coefficients. For BP Neural Network, RBF Neural Network has advantages on approximating ability and learning speed. So using RBF Neural Network as a recognition model is a good method. Experiments show that this new method has good robustness and adaptability. The speech recognition rate of this method reach ninety-one percents accurately under no noise environment. Speech signal recognition is feasible and effective in noisy environment.

Comparison between BP and RBF Neural Network Pattern Recognition Process Applied in the Droplet Analyzer

2014 ◽  
Vol 543-547 ◽  
pp. 2333-2336
Author(s):  
Qing Song ◽  
Gao Jie Meng ◽  
Lu Yang ◽  
Dan Qing Du ◽  
Xue Fei Mao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Liquid Droplet ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Uncertain System ◽  
Training Data ◽  
Waveform Analysis ◽  
Bp Network ◽  
Rbf Network

Among various pattern recognition methods used for liquid identification, the method based on neural network has the advantages of robustness and fault tolerance, which can study and adapt to the uncertain system. The waveform analysis is exploited for feature extraction of the liquid droplet fingerprint (LDF) in this paper, and the liquid identification is carried out by means of BP and RBF neural network. The experimental results proved that the recognition rate is excellent in both of these two methods. In condition that the training data is limited, RBF network is better than BP network in recognition speed and rate.

The RBF Neural Network Based on Kalman Filter Algorithm and Dual Radial Transfer Function

2014 ◽  
Vol 971-973 ◽  
pp. 1816-1819 ◽  
Author(s):  
Shao Yun Song ◽  
Bao Hua Zhang ◽  
Yu Ma
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Network Resource ◽  
Rbf Network ◽  
Kalman Filter Algorithm ◽  
The Common ◽  
Hidden Layer

RBF neural network have advantages of training simple, fast efficiency of learning, easy to fall into local minima, etc..It is widely used to solve the problem in signal processing and pattern recognition. Although the common RBF network is relatively easy to build, but because of the structure is usually fixed or high complexity, resulting in learning time is too long or network resource waste. For these reasons, proposed using extended Kalman filter as the RBF learning algorithm, and using double radial function in the hidden layer. By approaching the basis of the results of the analysis clearly shows that the network model than the other categories have a stronger generalization.

The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

2014 ◽  
Vol 596 ◽  
pp. 245-250
Author(s):  
Chun Feng Song ◽  
Yuan Bin Hou ◽  
Jing Yi Du
Keyword(s):  
Neural Network ◽  
Corrosion Rate ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Ant Colony ◽  
Rbf Network ◽  
Grounding Grid ◽  
Ant Colony Clustering ◽  
Hidden Layer

Because the grounding grid corrosion rate has the property of nonlinearity and uncertainty, it is very difficult for us to predict precisely. The approach is proposed that ant colony clustering algorithm is combined with RBF neural network to predict the grounding grid corrosion rate, using ant colony clustering algorithm to get the center of hidden layer neurons. To find the best clustering result, local search is applied in ant colony algorithm. This model has good performance of strong local generalization abilities and satisfying accuracy. At last, it is proved with lots of experiments that the application is fairly effective.

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