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https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/333
Title: | A Path Signature Approach for Speech Emotion Recognition |
Authors: | External author(s) only |
Keywords: | Emotions Mental Disorders Speech Emotion Recognition |
Issue Date: | Sep-2019 |
Citation: | Bo Wang, Maria Liakata, Hao Ni, Terry Lyons, Alejo J Nevado-Holgado, Kate Saunders. A Path Signature Approach for Speech Emotion Recognition. INTERSPEECH 2019 September 15–19, 2019 |
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. Index Terms: speech emotion recognition, path signature feature, convolutional neural network |
URI: | https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/333 |
ISSN: | 1990-9772 |
Appears in Collections: | Digital Medicine |
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