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A Path Signature Approach for Speech Emotion Recognition

Wang, B; Liakata, M; Ni, H; Lyons, T; Nevado-Holgado, AJ; Saunders, K; (2019) A Path Signature Approach for Speech Emotion Recognition. In: Proceedings Interspeech 2019. (pp. pp. 1661-1665). ISCA: Graz, Austria. Green open access

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Abstract

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.

Type: Proceedings paper
Title: A Path Signature Approach for Speech Emotion Recognition
Event: Interspeech 2019
Open access status: An open access version is available from UCL Discovery
DOI: 10.21437/interspeech.2019-2624
Publisher version: https://doi.org/10.21437/interspeech.2019-2624
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: speech emotion recognition, path signature feature, convolutional neural network
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
URI: https://discovery.ucl.ac.uk/id/eprint/10082916
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