Citation

Ong, Kah Liang and Lee, Chin Poo and Lim, Heng Siong and Lim, Kian Ming and Mukaida, Takeki (2023) SCQT-MaxViT: Speech Emotion Recognition With Constant-Q Transform and Multi-Axis Vision Transformer. IEEE Access, 11. pp. 63081-63091. ISSN 2169-3536

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Abstract

Speech emotion recognition presents a significant challenge within the field of affective computing, requiring the analysis and detection of emotions conveyed through speech signals. However, existing approaches often rely on traditional signal processing techniques and handcrafted features, which may not effectively capture the nuanced aspects of emotional expression. In this paper, an approach named “SCQT-MaxViT” is proposed for speech emotion recognition, combining signal processing, computer vision, and deep learning techniques. The method utilizes the Constant-Q Transform (CQT) to convert speech waveforms into spectrograms, providing high-frequency resolution and enabling the model to capture intricate emotional details. Additionally, the Multi-axis Vision Transformer (MaxViT) is employed for further representation learning and classification of the CQT spectrograms. MaxViT incorporates a multi-axis self-attention mechanism, facilitating both local and global interactions within the network and enhancing the ability of the model to learn meaningful features. Furthermore, the dataset is augmented using random time masking techniques to enhance the generalization capabilities. Achieving accuracies of 88.68% on the Emo-DB dataset, 77.54% on the RAVDESS dataset, and 62.49% on the IEMOCAP dataset, the proposed SCQT-MaxViT method exhibits promising performance in capturing and recognizing emotions in speech signals.

Item Type:	Article
Uncontrolled Keywords:	Speech emotion, deep learning techniques
Subjects:	B Philosophy. Psychology. Religion > BF Psychology (General) > BF1-990 Psychology Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2023 01:32
Last Modified:	01 Aug 2023 01:32
URII:	http://shdl.mmu.edu.my/id/eprint/11588

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