BEGAZO, Rolinson; AGUILERA, Ana; DONGO, Irvin; CARDINALE, Yudith

doi:10.3390/s24175797

DONGO, Irvin
ESTIA - Institute of technology [ESTIA]

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Article de revue

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Sensors. 2024-09-06, vol. 24, n° 17, p. 5797

Résumé en anglais

Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.< Réduire

Mots clés en anglais

Speech emotion recognition

Deep learning

Spectral features

Spectrogram imaging

Feature fusion

Convolutional neural network

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https://oskar-bordeaux.fr/handle/20.500.12278/205470

DOI

http://dx.doi.org/10.3390/s24175797

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ESTIA - Recherche

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A Combined CNN Architecture for Speech Emotion Recognition

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Résumé en anglais

Mots clés en anglais

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DOI

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