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Ensemble of Machine Learning Algorithms for Cognitive and Physical Speaker Load Detection
Abstract
We present our methods and results on participating in the Interspeech 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.
Figures
Training in our system consists of two stages.
Training in our system consists of two stages.
Keywords
Physical Load Detection | Cognitive Load Detection | Neural Network | Classification Models
Authors
Chi-Chun Lee
Publication Date
2014/09/14
Conference
Interspeech
Interspeech 2014
DOI
10.21437/Interspeech.2014-108
Publisher
ISCA