Ensemble of Machine Learning Algorithms for Cognitive and Physical Speaker Load Detection

被引：0

作者：

Jing, How ^{[1
]}

Hu, Ting-Yao ^{[2
]}

Lee, Hung-Shin ^{[3
]}

Chen, Wei-Chen ^{[4
]}

Lee, Chi-Chun ^{[4
]}

Tsao, Yu ^{[1
]}

Wang, Hsin-Min ^{[3
]}

机构：

[1] Acad Sinica, Res Ctr Informat Technol Innovat, Taipei, Taiwan

[2] Natl Taiwan Univ, Grad Inst Commun Engn, Taipei, Taiwan

[3] Acad Sinica, Inst Informat Sci, Taipei, Taiwan

[4] Natl Tsing Hua Univ, Dept Elect Engn, Hsinchu, Taiwan

来源：

15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4 | 2014年

关键词：

Physical Load Detection; Cognitive Load Detection; Neural Network; Classification Models;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present our methods and results on participating in the Inter speech 2014 Computational Paralinguistics ChallengE (ComParE) of which the goal is to detect certain type of load of a speaker using acoustic features. There are in total seven classification models contributing to our final prediction, namely, neural network with rectified linear unit and dropout (ReLUNet), conditional restricted Boltzmann machine (CRBM), logistic regression (LR), support vector machine (SVM), Gaussian discriminant analysis (GDA), k-nearest neighbors (KNN), and random forest (RF). When linearly blending the predictions of these models, we are able to get significant improvements over the challenge baseline.

引用

页码：447 / 451

页数：5

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