GAMBOA, Alvaro; DONGO, Irvin; AGUILERA, Ana; BEGAZO, Rolinson

doi:10.1109/clei64178.2024.10700306

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

GAMBOA, Alvaro

DONGO, Irvin
ESTIA - Institute of technology [ESTIA]

AGUILERA, Ana

Langue

Communication dans un congrès

Ce document a été publié dans

IEEE Conference on Artificial Intelligence, CAI 2023. Proceedings, 2024 L Latin American Computer Conference (CLEI), 2024-08-12, Buenos Aires. p. 1-10

IEEE

Résumé en anglais

Nowadays, the advancement of technology allows the use of social robots for various daily tasks such as therapies, teaching assistants, restaurant services, among others. Human-Robot Interaction (HRI) is under constant study due to the new capabilities that robots acquire thanks to their improved hardware (e.g., more joints). Robots receive information through sensors such as cameras and microphones and can thus modify their behavior and adapt to different situations. However, an exhaustive real-time analysis of data within the robot requires excessive computing power and energy usage, which are limited in social robots. In this context, we propose a lightweight Machine Learning model to balance accuracy and audio processing time to recognize the emotions of happiness, sadness, anger, and neutral in real-time, aiming to improve HRI. Additionally, an empirical analysis to identify the most relevant audio features for emotion recognition is presented. The objective is to generate a lighter and more appropriate model for the robot's hardware. Results show better accuracy by using the RAVDESS, IEMOCAP, and RAVDESS+IEMOCAP datasets and a recognition time around 1 second.< Réduire

Mots clés en anglais

Emotion

Machine Learning

Social Robots

Speech Emotion Recognition

Métadonnées

Partager cette publication !

Licence d’utilisation du document

Towards Speech Emotion Recognition Applied to Social Robots

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

URI

DOI

Unités de recherche