Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops pp. 511 - 516
• Main Authors: Jun Deng, Zixing Zhang, Marchi, Erik, Schuller, Bjorn
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2013
• Subjects: Acoustics; deep neural networks; Emotion recognition; sparse autoencoder; Speech; speech emotion recognition; Speech recognition; Training; transfer learning
• DOI: 10.1109/ACII.2013.90
• ISSN: 2156-8103

In speech emotion recognition, training and test data used for system development usually tend to fit each other perfectly, but further 'similar' data may be available. Transfer learning helps to exploit such similar data for training despite the inherent dissimilarities in order to boost a recogniser's performance. In this context, this paper presents a sparse auto encoder method for feature transfer learning for speech emotion recognition. In our proposed method, a common emotion-specific mapping rule is learnt from a small set of labelled data in a target domain. Then, newly reconstructed data are obtained by applying this rule on the emotion-specific data in a different domain. The experimental results evaluated on six standard databases show that our approach significantly improves the performance relative to learning each source domain independently.