  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.
