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A two-level TDNN (TLTDNN) technique for large vocabulary Mandarin final recognition

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1 Author(s)
Gee-Swee Poo ; Dept. of Inf. Syst. & Comput. Sci., Nat. Univ. of Singapore, Singapore

A two-level time-delay neural network (TLTDNN) technique has been developed to recognize all Mandarin finals of the entire Chinese syllables. The first level discriminates the vowel-group (a,e,i,o,u,v) and the nasal-group based on nasal ending, (-n,-ng,-others). Orthogonal combination of the two groupings in the first level enables the second level discrimination of all 35 Mandarin finals. The technique was thoroughly tested with 8 sets of 1265 isolated Hanyu pinyin syllables, with 6 sets used for training and 2 sets used for testing. The overall result shows that a high recognition rate of 95.3% for inside testing and 93.9% for outside testing is achievable

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994

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