The Journal of the Acoustical Society of Korea

한국음향학회 (The Acoustical Society of Korea)
권호 표지

Stereo Vision Neural Networks with Competition and Cooperation for Phoneme Recognition

  • Kim, Sung-Ill (Division of Electrical and Electronic Engineering, Kyungnam University) ;
  • Chung, Hyun-Yeol (School of Electrical Engineering and Computer Science, Yeungnam University)
  • 발행 : 2003.03.01
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초록

This paper describes two kinds of neural networks for stereoscopic vision, which have been applied to an identification of human speech. In speech recognition based on the stereoscopic vision neural networks (SVNN), the similarities are first obtained by comparing input vocal signals with standard models. They are then given to a dynamic process in which both competitive and cooperative processes are conducted among neighboring similarities. Through the dynamic processes, only one winner neuron is finally detected. In a comparative study, with, the average phoneme recognition accuracy on the two-layered SVNN was 7.7% higher than the Hidden Markov Model (HMM) recognizer with the structure of a single mixture and three states, and the three-layered was 6.6% higher. Therefore, it was noticed that SVNN outperformed the existing HMM recognizer in phoneme recognition.

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참고문헌

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