標題: CMOS CURRENT-MODE IMPLEMENTATION OF SPATIOTEMPORAL PROBABILISTIC NEURAL NETWORKS FOR SPEECH RECOGNITION
作者: WU, CY
LIU, RY
JOU, IC
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
公開日期: 1-六月-1995
摘要: In this paper, a Spatiotemporal Probabilistic Neural Network (SPNN) is proposed for spatiotemporal pattern recognition. This new model is developed by applying the concept of Gaussian density function to the network structure of the SPR (SPatiotemporal Pattern Recognition). The main advantages of this model include faster training and recalling process for patterns. In addition, the overall architecture is also simple, modular, regular, locally connected, and suitable for VLSI implementation. One set of independent speaker isolated (Mandarin digit) speech database is used as an example to demonstrate the superiority of the neural networks for spatiotemporal pattern recognition. The testing result with a reduced error rate of 7% shows that the SPNN is very attractive and effective for practical applications. The CMOS current-mode IC technology is used to implement the SPNN to achieve the objective of minimum classification error ina more direct manner. In this design, neural computation is performed in analog circuits while template information is stored in digital circuits. The prototyping speech recognition processor for the 12th LPC calculation is designed by 1.2 mu m CMOS technology. The HSPICE simulation results are also presented, which verifies the function of the designed neural system.
URI: http://dx.doi.org/10.1007/BF02407027
http://hdl.handle.net/11536/1906
ISSN: 0922-5773
DOI: 10.1007/BF02407027
期刊: JOURNAL OF VLSI SIGNAL PROCESSING
Volume: 10
Issue: 1
起始頁: 67
結束頁: 84
顯示於類別:期刊論文