Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition
Year: 2013
Type: conference-paper
Abstract: 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.
DOI: 10.1109/acii.2013.90
Authors: Jun Deng, Zixing Zhang, Erik Marchi, Bjorn Schuller
Institutions: Technical University of Munich, University of Passau
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2050752817