Cognitive Load (CL) refers to the amount of mental demand that a given task imposes on an individual’s cognitive system and it can affect his/her productivity in very high load situations. In this paper, we propose an automatic system capable of classifying the CL level of a speaker by analyzing his/her voice. Our research on this topic goes into two main directions. In the first one, we focus on the use of Long Short-Term Memory (LSTM) networks with different weighted pooling strategies for CL level classification. In the second contribution, for overcoming the need of a large amount of training data, we propose a novel attention mechanism that uses the Kalinli’s auditory saliency model. Experiments show that our proposal outperforms significantly both, a baseline system based on Support Vector Machines (SVM) and a LSTM-based system with logistic regression attention model.
Cite as: Gallardo-Antolín, A., Montero, J.M. (2019) A Saliency-Based Attention LSTM Model for Cognitive Load Classification from Speech. Proc. Interspeech 2019, 216-220, doi: 10.21437/Interspeech.2019-1603
@inproceedings{gallardoantolin19_interspeech,
author={Ascensión Gallardo-Antolín and Juan Manuel Montero},
title={{A Saliency-Based Attention LSTM Model for Cognitive Load Classification from Speech}},
year=2019,
booktitle={Proc. Interspeech 2019},
pages={216--220},
doi={10.21437/Interspeech.2019-1603}
}