RT Conference Proceedings
T1 A saliency-based attention LSTM model for cognitive load classification from speech
A1 Gallardo Antolín, Ascensión
A1 Montero Martínez, Juan Manuel
AB 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.
PB International Speech Communication Association (ISCA)
YR 2019
FD 2019
LK https://hdl.handle.net/10016/31660
UL https://hdl.handle.net/10016/31660
LA eng
NO Proceeding of: Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019
NO The work leading to these results has been partly supported by Spanish Government grants TEC2017-84395-P and TEC2017-84593-C2-1-R.
DS e-Archivo
RD 1 jul. 2024