TY - GEN
T1 - Speech Emotion Recognition Using 1D/2D Convolutional Neural Networks
AU - Larisa, Pencea Maria
AU - Tapu, Ruxandra
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Over the last few decades, emotion recognition has been a hot topic of research in the affective computing community. The automatic identification of emotions from raw speech signals is highly challenging and depends on multiple factors, including: the utterance length, the speaker language, gender or accent. In addition, the process is highly subjective because people can perceive emotions differently. In that regard, the goal of this paper is to evaluate some state-of-the-art deep convolutional neural networks (DCNNs) architectures receiving as input various ID/2D speech feature representations, conduct experiments on a publicly available dataset (Ryerson Audio-Visual Database of Emotional Speech and Song Dataset - RA VDESS) and identify which architecture has the best performance in the discrete emotion classification task.
AB - Over the last few decades, emotion recognition has been a hot topic of research in the affective computing community. The automatic identification of emotions from raw speech signals is highly challenging and depends on multiple factors, including: the utterance length, the speaker language, gender or accent. In addition, the process is highly subjective because people can perceive emotions differently. In that regard, the goal of this paper is to evaluate some state-of-the-art deep convolutional neural networks (DCNNs) architectures receiving as input various ID/2D speech feature representations, conduct experiments on a publicly available dataset (Ryerson Audio-Visual Database of Emotional Speech and Song Dataset - RA VDESS) and identify which architecture has the best performance in the discrete emotion classification task.
KW - affective networks
KW - deep convolutional neural networks
KW - speech emotion recognition
U2 - 10.1109/ISETC56213.2022.10010227
DO - 10.1109/ISETC56213.2022.10010227
M3 - Conference contribution
AN - SCOPUS:85147550664
T3 - 2022 15th International Symposium on Electronics and Telecommunications, ISETC 2022 - Conference Proceedings
BT - 2022 15th International Symposium on Electronics and Telecommunications, ISETC 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Symposium on Electronics and Telecommunications, ISETC 2022
Y2 - 10 November 2022 through 11 November 2022
ER -
