Real Time Speech Emotion Recognition Using RGB Image Classification and Transfer Learning

Stolar, M, Lech, M, Bolia, R and Skinner, M 2017, 'Real Time Speech Emotion Recognition Using RGB Image Classification and Transfer Learning', in Tadeusz A Wysocki & Beata J Wysocki (ed.) Proceedings of the 11th International Conference on Signal Processing and Communication Systems (ICSPCS 2017), Queensland, Australia, 13-15 December 2017, pp. 21-28.


Document type: Conference Paper
Collection: Conference Papers

Title Real Time Speech Emotion Recognition Using RGB Image Classification and Transfer Learning
Author(s) Stolar, M
Lech, M
Bolia, R
Skinner, M
Year 2017
Conference name ICSPCS 2017
Conference location Queensland, Australia
Conference dates 13-15 December 2017
Proceedings title Proceedings of the 11th International Conference on Signal Processing and Communication Systems (ICSPCS 2017)
Editor(s) Tadeusz A Wysocki & Beata J Wysocki
Publisher IEEE
Place of publication United States
Start page 21
End page 28
Total pages 8
Abstract This paper describes a real-time Speech Emotion Recognition (SER) task formulated as an image classification problem. The shift to an image classification paradigm provided the advantage of using an existing Deep Neural Network (AlexNet) pre-trained on a very large number of images, and thus eliminating the need for a lengthy network training process. Two alternative multi-class SER systems, AlexNet-SVM and FTAlexNet, were investigated. Both systems were shown to achieve state-of-the-art results when tested on a popular Berlin Emotional Speech (EMO-DB) database. Transformation from speech to image classification was achieved by creating RGB images depicting speech spectrograms. The ALEXNet-SVM method passes the spectrogram images as inputs to a pre-trained Convolutional Neural Network (AlexNet) to provide features for the Support Vector Machine (SVM) classifier, whereas the FTAlexNet method simply applies the images to a fine tuned AlexNet to provide emotional class labels. The FTAlexNet offers slightly higher accuracy compared to the AlexNet-SVM, while the AlexNet-SVM requires a lower number of computations due to the elimination of the neural network training procedure. A real-time demo is given on: https://www.youtube.com/watch?v=fuMpF3cUqDU&t=6s.
Subjects Signal Processing
Keyword(s) real time speech emotion recognition
visual speech classification
deep learning
transfer learning
neural networks
AlexNet
spectrograms
DOI - identifier 10.1109/ICSPCS.2017.8270472
Copyright notice © 2017 IEEE
ISBN 9781538628874
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