Abstract
Affective computing comprises the techniques devoted to identify and understand human emotions. However, this topic covers many other subtopics; it can be remarked Speech Emotion Recognition (SER) between them. In the last two decades, we have witnessed the birth and expansion of marketed products like smart voice assistants and their associated autonomous smart speakers by Amazon, Google, and Apple. This work presents the design and implementation of a new Emotional Smart Speaker prototype-based hybridisation of an Amazon Echo Dot device and A Rasberry PI with a low-power SER algorithm built-in. The proposed SER algorithm is based on a Bag of Models method with two base models, an XtraTrees algorithm and a pre-trained Resnet18 Neural Network. The proposal has been validated for four well-known SER datasets: EmoDB, TESS, SAVEE and RAVDSS. And the obtained model outperforms eleven well-known ML methods available in the literature for the studied public datasets.
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This research has been funded partially by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) under grant TIN2017-84804-R/PID2020-112726RB-I00.
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de la Cal, E., Gallucci, A., Villar, J.R., Yoshida, K., Koeppen, M. (2022). A First Prototype of an Emotional Smart Speaker. In: Sanjurjo González, H., Pastor López, I., GarcÃa Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_29
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