CMOS implementation of neural networks for speech recognition

I. Chang Jou*, Ron Yi Liu, Chung-Yu Wu

*Corresponding author for this work

    Research output: Contribution to conferencePaperpeer-review

    3 Scopus citations


    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 new model include faster training and recalling process for patterns, and the overall architecture is also simple, modular, regular, locally connected for VLSI implementation. The CMOS current-mode IC technology is used to implement the SPNN to achieve the objective of minimum classification error in a more direct manner. In this design, neural computation is performed in analog circuits while template information is stored in digital circuits. 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.

    Original languageEnglish
    Number of pages6
    StatePublished - 1 Dec 1994
    EventProceedings of the 1994 IEEE Asia-Pacific Conference on Circuits and Systems - Taipei, Taiwan
    Duration: 5 Dec 19948 Dec 1994


    ConferenceProceedings of the 1994 IEEE Asia-Pacific Conference on Circuits and Systems
    CityTaipei, Taiwan


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