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Prediction of Personal Characteristics and Emotional State based on Voice Signals using Machine Learning Techniques
Topics: Detection and Identification; Multimodal Speech & Audio Processing; Pattern Recognition & Machine Learning for Biosignal Data
Marta Babel Guerreiro 1 ; Catia Cepeda 1 ; Joana Sousa 2 ; 3 ; Carolina Maio 2 ; João Ferreira 2 and Hugo Gamboa 1
Affiliations: 1 LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal ; 2 NOS Inovação, Lisboa, Portugal ; 3 Bold International, Lisboa, Portugal
Keyword(s): Gender, Age, Emotion, Machine Learning, Voice Signal.
Abstract: Voice signals are a rich source of personal information, leading to the main objective of the present work: study the possibility of predicting gender, age, and emotional valence through short voice interactions with a mobile device (a smartphone or remote control), using machine learning algorithms. For that, data acquisition was carried out to create a Portuguese dataset (consisting in 156 samples). Testing Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers and using features extracted from the audio, the gender recognition model achieved an accuracy of 87.8%, the age group recognition model achieved an accuracy of 67.6%, and an accuracy of 94.6% was reached for the emotion model. The SVM algorithm produced the best results for all models. The results show that it is possible to predict not only someone’s specific personal characteristics but also its emotional state from voice signals. Future work should be done in order to improve these mo dels by increasing the dataset. (More)
Voice signals are a rich source of personal information, leading to the main objective of the present work: study the possibility of predicting gender, age, and emotional valence through short voice interactions with a mobile device (a smartphone or remote control), using machine learning algorithms. For that, data acquisition was carried out to create a Portuguese dataset (consisting in 156 samples). Testing Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers and using features extracted from the audio, the gender recognition model achieved an accuracy of 87.8%, the age group recognition model achieved an accuracy of 67.6%, and an accuracy of 94.6% was reached for the emotion model. The SVM algorithm produced the best results for all models. The results show that it is possible to predict not only someone’s specific personal characteristics but also its emotional state from voice signals. Future work should be done in order to improve these models by increasing the dataset.
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Paper citation in several formats:
Guerreiro, M. B., Cepeda, C., Sousa, J., Maio, C., Ferreira, J. and Gamboa, H. (2022). Prediction of Personal Characteristics and Emotional State based on Voice Signals using Machine Learning Techniques. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 142-149. DOI: 10.5220/0010802700003123
@conference{biosignals22,
author={Marta Babel Guerreiro and Catia Cepeda and Joana Sousa and Carolina Maio and João Ferreira and Hugo Gamboa},
title={Prediction of Personal Characteristics and Emotional State based on Voice Signals using Machine Learning Techniques},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS},
year={2022},
pages={142-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010802700003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}
TY - CONF
JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS
TI - Prediction of Personal Characteristics and Emotional State based on Voice Signals using Machine Learning Techniques
SN - 978-989-758-552-4
IS - 2184-4305
AU - Guerreiro, M.
AU - Cepeda, C.
AU - Sousa, J.
AU - Maio, C.
AU - Ferreira, J.
AU - Gamboa, H.
PY - 2022
SP - 142
EP - 149
DO - 10.5220/0010802700003123
PB - SciTePress