Multi-Window Data Augmentation Approach for Speech Emotion Recognition
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We present a novel, Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition. MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method to generate additional data samples and building the deep learning models to recognize the underlying emotion of an audio signal. The multi-window augmentation method extracts more audio features from the speech signal by employing multiple window sizes in the audio feature extraction process. We show that our proposed augmentation method, combined with a deep learning model, improves the speech emotion recognition performance. We evaluate the performance of our MWA-SER approach on the IEMOCAP corpus and show that our proposed method achieves state-of-the-art results. Furthermore, the proposed system demonstrated 70 while recognizing the emotions for the SAVEE and RAVDESS datasets, respectively.
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