Implementing PCA-based speaker adaptation methods in a Persian ASR system

Ansari, Zohreh; Almasganj, Farshad

doi:10.1109/istel.2010.5734126

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…1) Calculate the square of the distance between the two classes and the square of intra-class scatter , by Eqs. (14), (15) and 16; 2) Construct Fisher discriminate function ( ) by Eq. 17, and regard it as a fitness for WPSO;…”

Section: The Steps Optimized By Wpso-fda For Best Solutionmentioning

confidence: 99%

“…Particle swarm optimization (PSO) [8] is a novel global parallel optimization algorithm, which is inspired by the biological and sociological behavior of animal swarms searching for food. More recently, various PSO and improved PSO algorithms as a type of novel evolutionary computation techniques have been developed and applied to a wide range of optimization problems [9,10], such as function optimization [11], automatic controlling [12], robotic learning, artificial life [13] and other fields [14,15]. Compared with genetic algorithm (GA), the PSO algorithm has no complicated evolutionary operators such as crossover and mutation so that it can be computed efficiently and implemented conveniently.…”

Section: Introductionmentioning

confidence: 99%

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Regularized kernel function parameter of KPCA using WPSO-FDA for feature extraction and fault recognition of gearbox

He¹,

Wang²

2018

J. vibroeng.

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Gearbox is subject to damage or malfunctions by complicated factors such as installation position and operation condition, meanwhile, accompanied by some nonlinear behaviors, which increase the difficulty of fault diagnosis and identification. Kernel principal component analysis (KPCA) is a commonly used method to realize nonlinear mapping via kernel function for feature extraction. However, choosing an appropriate kernel function and the proper setting of its parameter are decisive to obtain a high performance of the kernel methods. In this paper, we present a novel approach combining PSO and KPCA to enhance the fault classification performance. The standard particle swarm optimization (WPSO) was used to regularize kernel function parameter of KPCA instead of the empirical value. In particular, in view of the thought of Fisher Discriminate Analysis (FDA) in pattern recognition, the optimal mathematical model of kernel parameter was constructed, and its global optimal solution was searched by WPSO. The effectiveness of the method was proven using the Iris data set classification and gearbox faults classification. In the process, gearbox fault experiments were carried out, and the vibration signals in different conditions have been tested and processed, and the fault feature parameters were extracted. At last the analysis results of gearbox fault recognition was obtained by KPCA and compared with PCA. The results show that the separability of failure patterns in the feature space is improved after kernel parameter optimized by WPSO-FDA. The problems of single failure and compound fault recognition have been effectively solved by the optimized KPCA.

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Section: The Steps Optimized By Wpso-fda For Best Solutionmentioning

confidence: 99%