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Descripción
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| 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. We focus on the use of Long Short-Term Memory (LSTM) networks with different weighted pooling strategies, such as mean-pooling, max-pooling, last-pooling and a logistic regression attention model. In addition, as an alternative to the previous methods, we propose a novel attention mechanism, called external attention model, that uses external cues, such as log-energy and fundamental frequency, for weighting the contribution of each LSTM temporal frame, overcoming the need of a large amount of data for training the attentional model. Experiments show that the LSTM-based system with external attention model outperforms significantly the baseline system based on Support Vector Machines (SVM) and the LSTM-based systems with the conventional weighed pooling schemes and with the logistic regression attention model | |
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Internacional
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Si |
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Nombre congreso
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7th International Conference on Statistical Language and Speech Processing (SLSP 2019) |
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Tipo de participación
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960 |
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Lugar del congreso
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Ljubliana |
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Revisores
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Si |
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ISBN o ISSN
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1611-3349 |
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DOI
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Fecha inicio congreso
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14/10/2019 |
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Fecha fin congreso
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16/10/2019 |
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Desde la página
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139 |
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Hasta la página
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150 |
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Título de las actas
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Actas del 7th International Conference on Statistical Language and Speech Processing (SLSP 2019) |