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Evaluating deep learning architectures for Speech Emotion Recognition
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DOI：10.1016/j.neunet.2017.02.013.png)
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Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Affective computing
Deep learning
Emotion recognition
Neural networks
Speech recognition
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Survey on speech emotion recognition: Features, classification schemes, and databases.png)
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