An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance

• URL: http://arxiv.org/abs/2312.10937v1
• Date: Mon, 18 Dec 2023 05:24:03 GMT
• Title: An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
• Authors: David Hason Rudd, Huan Huo, Guandong Xu
• Abstract summary: This study proposes VGG-optiVMD, an empowered variational mode decomposition algorithm, to distinguish meaningful speech features. Various feature vectors were employed to train the VGG16 network on different databases and assess VGG-optiVMD and reliability. Results confirmed a synergistic relationship between the fine-tuning of the signal sample rate and decomposition parameters with classification accuracy.
• Score: 15.919990281329085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlying the emotions are significant for human-computer interactions enabling intelligent machines to interact with sensitivity in the real world. Previous ER studies through speech signal processing have focused exclusively on associations between different signal mode decomposition methods and hidden informative features. However, improper decomposition parameter selections lead to informative signal component losses due to mode duplicating and mixing. In contrast, the current study proposes VGG-optiVMD, an empowered variational mode decomposition algorithm, to distinguish meaningful speech features and automatically select the number of decomposed modes and optimum balancing parameter for the data fidelity constraint by assessing their effects on the VGG16 flattening output layer. Various feature vectors were employed to train the VGG16 network on different databases and assess VGG-optiVMD reproducibility and reliability. One, two, and three-dimensional feature vectors were constructed by concatenating Mel-frequency cepstral coefficients, Chromagram, Mel spectrograms, Tonnetz diagrams, and spectral centroids. Results confirmed a synergistic relationship between the fine-tuning of the signal sample rate and decomposition parameters with classification accuracy, achieving state-of-the-art 96.09% accuracy in predicting seven emotions on the Berlin EMO-DB database.

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