Neural network and GMM based feature mappings for consonant–vowel recognition in emotional environment

In this work, we propose a mapping function based feature transformation framework for developing consonant–vowel (CV) recognition system in the emotional environment. An effective way of conveying messages is by expressing emotions during human conversations. The characteristics of CV units differ from one emotion to other emotions. The performance of existing CV recognition systems is degraded in emotional environments. Therefore, we have proposed mapping functions based on artificial neural network and GMM models for increasing the accuracy of CV recognition in the emotional environment. The CV recognition system has been explored to transform emotional features to neutral features using proposed mapping functions at CV and phone levels to minimize mismatch between training and testing environments. Vowel onset and offset points have been used to identify vowel, consonant and transition segments. Transition segments are identified by considering initial 15% speech samples between vowel onset and offset points. The average performance of CV recognition system is increased significantly using feature mapping technique at phone level in three emotional environments (anger, happiness, and sadness).     

A solving method based on neural network for a class of multi-leader–follower games

In this paper, we present a new solving approach for a class of multi-leader–follower games. For the problem studied, we firstly propose a neural network model. Then, based on Lyapunov and LaSalle theories, we prove that the trajectory of the neural network model can converge to the equilibrium point, which corresponds to the Nash equilibrium of the problem studied. The numerical results show that the proposed neural network approach is feasible to the problem studied.

     

Gas recognition algorithm applied in porphyrin array gas detection system北大核心CSCD

The difference map is the basis of identifying gases by the PSA chips. However, there are differences between each difference map of a gas, which is called the“divergent problem”. A pattern recognition algorithm based on backpropagation neural network and rough set was described, which was employed in the porphyrin chemical sensor array integrated system. That algorithm picked up the spots whose color changed obviously using the rough set, and set their values as input of BP network. Comparing with the result of Euclidean distance clustering and BP neural network identification without removing unnecessary data as input, the result of the algorithm proposed in this article has higher identification accuracy to the divergence experimental data. ©, 2014, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

Parallel processing of chemical information in a local area network—II. A parallel cross-validation procedure for artificial neural networks

This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper. A brief theoretical introduction on MLF networks is given and the problems, associated with the validation of predictive abilities, will be discussed. Furthermore, this paper comprises a general outline of the PCV program. Finally, the parallel PCV application is used to validate the predictive ability of an MLF network modeling a chemical non-linear function approximation problem which is described extensively in the literature.     

Hybridized estimations of support vector machine free parameters C and γ using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment

The support vector machine (SVM) is a popular classification model for speaker verification. However, although SVM is suitable for classifying speakers, the uncertain values of the free parameters C and γ of the SVM model have been a challenging technique problem. An improper value set provided for the free parameter pair (C, γ) can cause dissatisfactory performance in the recognition accuracy of speaker verification. Moreover, the sound source localization information of the collected acoustic data has a large effect on the recognition performance of SVM speaker verification. In response, this study developed a sound source localization-driven fuzzy scheme to help determine the optimal value set of (C, γ) for the establishment of an SVM model. Specifically, this scheme adopts the estimated information of time difference of arrival (TDOA) derived from the Kinect microphone array (containing both the angle and distance information of the acoustic data of the speaker), to optimally calculate the value set of the SVM free parameters C and γ. It was demonstrated that speaker verification using the SVM with a properly estimated parameter pair (C, γ) is more accurate than that with only an arbitrarily given value set for the parameter pair (C, γ) on recognition rate.