Journal title
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
DOI
10.21437/Interspeech.2019-2624
Volume
2019-September
Last updated
2024-04-07T21:36:20.287+01:00
Page
1661-1665
Abstract
Copyright © 2019 ISCA Automatic speech emotion recognition (SER) remains a difficult task within human-computer interaction, despite increasing interest in the research community. One key challenge is how to effectively integrate short-term characterisation of speech segments with long-term information such as temporal variations. Motivated by the numerical approximation theory of stochastic differential equations (SDEs), we propose the novel use of path signatures. The latter provide a pathwise definition to solve SDEs, for the integration of short speech frames. Furthermore we propose a hierarchical tree structure of path signatures, to capture both global and local information. A simple tree-based convolutional neural network (TBCNN) is used for learning the structural information stemming from dyadic path-tree signatures. Our experimental results on a widely used benchmark dataset demonstrate comparable performance to complex neural network based systems.
Symplectic ID
1073987
Submitted to ORA
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Publication type
Conference Paper