Abstract
The fast Continuous Wavelet Transform (fCWT) is used to improve Deep Convolutional Neural Networks (DCNN)’s Speech Emotion Recognition (SER). While being computationally efficient, the fCWT’s time-frequency analysis overcomes traditional methods’ resolution limitations (e.g., Short-Term Fourier Transform). fCWT-induced DCNNs are compared to state-of-the-art DCNN SER systems. Comparing different wavelet parameters, we also provide an empirical strategy for balancing temporal and spectral features in speech signals. We suggest that this strategy is of generic interest for non-stationary signal processing where large amounts of data are available. fCWT’s potential for improving SER accuracy in real-time applications is confirmed. In parallel, the variance in the cross-validation folds confirmed deep learning’s vulnerability on non-big data sets.
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