TY - GEN
T1 - Robust recognition of Mandarin vowels by articulatory manners
AU - Hu, Jin
AU - Liu, Jing
AU - Zhang, Yingnan
AU - Zha, Zhuanling
AU - Xie, Xiang
AU - Huang, Shilei
PY - 2017/1/13
Y1 - 2017/1/13
N2 - This paper proposes a robust classifier for Mandarin vowels considering articulatory manners (AMs) which include the height of the body of the tongue, the front-back position of the tongue, and the degree of lip rounding. Firstly, the articulatory manners of each vowel are encoded to a 3-dimension vector pattern. Then, acoustic features are extracted and mapped to the articulatory manner vector by ELM. Finally, the nearest vowel to the articulatory manner vector is chosen as the recognized result. Comparison between our method and the direct method without considering the articulatory manners shows that the proposed method has an improvement of 7.1 percentage points. Tests with three kinds of noisy data in the Aurora-4 show it also outperforms the normal method with an about a gain of about 4 percentage points.
AB - This paper proposes a robust classifier for Mandarin vowels considering articulatory manners (AMs) which include the height of the body of the tongue, the front-back position of the tongue, and the degree of lip rounding. Firstly, the articulatory manners of each vowel are encoded to a 3-dimension vector pattern. Then, acoustic features are extracted and mapped to the articulatory manner vector by ELM. Finally, the nearest vowel to the articulatory manner vector is chosen as the recognized result. Comparison between our method and the direct method without considering the articulatory manners shows that the proposed method has an improvement of 7.1 percentage points. Tests with three kinds of noisy data in the Aurora-4 show it also outperforms the normal method with an about a gain of about 4 percentage points.
KW - Articulatory manners
KW - ELM
KW - Mandarin vowel recognition
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=85018686801&partnerID=8YFLogxK
U2 - 10.1145/3036290.3036314
DO - 10.1145/3036290.3036314
M3 - Conference contribution
AN - SCOPUS:85018686801
T3 - ACM International Conference Proceeding Series
SP - 172
EP - 175
BT - Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017
Y2 - 13 January 2017 through 16 January 2017
ER -