시공간 특성을 이용한 지역명 인식에 대한 연구

A Study on the Recognition of Spoken Korean Citynames Using Spatio-Tenporal

This paper is about an experiment of speaker-dependent automatic Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The object words are 50 citynames of D.D.D. local numbers. 43 of those are 2 syllables and the rest 7 are 3 syllables. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. That led independent result on the speech duration, and the PARCOR coefficients calculated from the frames using linear predictive analysis were employed as feature components. This paper tried to find out the optimum conditions through 4 differerent experiment which are comparison between total and pre-classified training, dependency of recognition rate on the number of frames and PARCOR order, recognition change due to the number of neurons in the hidden layer, and the comparison of the output pattern composition method of output neurons. Improving this research, real-time speaker-independent small-vocabulary automatic Korean speech recognition will be possible.