Abstract
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. We propose a novel multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process. We show that our proposed augmentation method with minimally extracted features combined with a deep learning model improves the performance of speech emotion recognition. We demonstrate the performance of our MWA-SER approach on the IEMOCAP corpus and show that our approach outperforms previous methods, exhibiting 65% accuracy and 73% weighted average precision, a 6% and a 9% absolute improvements on accuracy and weighted average precision, respectively. We also demonstrate that with the minimum number of features (34), our model outperforms other models that use more than 900 features with higher modeling complexity. Furthermore, we also evaluate our model by replacing the "happy" category of emotion with "excited". To the best of our knowledge, our approach achieves state-of-the-art results with 66% accuracy and 68% weighted average precision, which is an 11% and a 14% absolute improvement on accuracy and weighted average precision, respectively.
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URL
https://arxiv.org/abs/2010.09895
