Title :
A two-level TDNN (TLTDNN) technique for large vocabulary Mandarin final recognition
Author_Institution :
Dept. of Inf. Syst. & Comput. Sci., Nat. Univ. of Singapore, Singapore
fDate :
27 Jun-2 Jul 1994
Abstract :
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
Keywords :
neural nets; speech recognition; Hanyu pinyin syllables; Mandarin final; nasal-group; speech recognition; two-level time-delay neural network; vowel-group; Computer science; Databases; Information systems; Natural languages; Neural networks; Speech recognition; Testing; Time domain analysis; Vector quantization; Vocabulary;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374976
