title: Evaluating deep learning architectures for Speech Emotion Recognition.
abstract: 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.
authors.affiliation: School of Engineering, RMIT University, Melbourne VIC 3001, Australia. Electronic address: haytham.fayek@ieee.org.
authors.forename: Haytham M
authors.initials: HM
authors.lastname: Fayek
authors.affiliation: School of Engineering, RMIT University, Melbourne VIC 3001, Australia. Electronic address: margaret.lech@rmit.edu.au.
authors.forename: Margaret
authors.initials: M
authors.lastname: Lech
authors.affiliation: School of Science, RMIT University, Melbourne VIC 3001, Australia. Electronic address: lawrence.cavedon@rmit.edu.au.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Cavedon
mesh_terms: D004644:Emotions; D000069550:Machine Learning; D016571:Neural Networks, Computer; D049250:Speech Recognition Software
keywords: Affective computing; Deep learning; Emotion recognition; Neural networks; Speech recognition