An efficient hybrid learning algorithm for neural network–based speech recognition systems on FPGA chip

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• 作者：Shing-Tai Pan (1)
  Min-Lun Lan (2)
  
• 关键词：Field programmable gate array ; Genetic algorithm ; Hybrid learning algorithm ; Speech recognition
• 刊名：Neural Computing & Applications
• 出版年：2014
• 出版时间：June 2014
• 年：2014
• 卷：24
• 期：7-8
• 页码：1879-1885
• 全文大小：
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• 作者单位：Shing-Tai Pan (1)
  Min-Lun Lan (2)
  
  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nanzih Dist., Kaohsiung, 811, Taiwan, ROC
  2. Institute of Computer Science and Information Engineering, Shu-Te University, Kaohsiung, 824, Taiwan, ROC
  
• ISSN：1433-3058

文摘

This paper implemented an artificial neural network (ANN) on a field programmable gate array (FPGA) chip for Mandarin speech measurement and recognition of nonspecific speaker. A three-layer hybrid learning algorithm (HLA), which combines genetic algorithm (GA) and steepest descent method, was proposed to fulfill a faster global search of optimal weights in ANN. Some other popular evolutionary algorithms, such as differential evolution, particle swarm optimization and improve GA, were compared to the proposed HLA. It can be seen that the proposed HLA algorithm outperforms the other algorithms. Finally, the designed system was implemented on an FPGA chip with an SOC architecture to measure and recognize the speech signals.