Fuzzy-Neural-Network Based Audio-Visual Fusion for Speech Recognition

被引：0
|
作者：
Wu, Gin-Der [1 ]
Tsai, Hao-Shu [1 ]
机构：
[1] Natl Chi Nan Univ, Dept Elect Engn, Nantou, Taiwan
来源：
2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019) | 2019年
关键词：
speech recognition; classification; type-2 fuzzy sets; linear-discriminant-analysis; discriminability;
D O I：
10.1109/icaiic.2019.8669019
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Speech recognition is an important classification problem in signal processing. Its performance is easily affected by noisy environment due to movements of desks, door slams, etc. To solve the problem, a fuzzy-neural-network based audio-visual fusion is proposed in this study. Since human speech perception is bimodal, the input features include both audio and image information. In the fuzzy-neural-network, type-2 fuzzy sets are used in the antecedent parts to deal with the noisy data. Furthermore, a linear-discriminant-analysis (LDA) is applied in to the consequent parts to increase the "discriminability". Compared with pure audio-based speech recognition, the fuzzy-neural-network based audio-visual fusion method is more robust in noisy environment.
引用
收藏
页码：210 / 214
页数：5
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