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- W2973161331 abstract "Automatic speech emotion recognition (SER) remains adifficult task within human-computer interaction, despite increasing interest in the research community. One key challenge is how to effectively integrate short-term characterisationof speech segments with long-term information such as temporal variations. Motivated by the numerical approximation theory of stochastic differential equations (SDEs), we propose thenovel 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 usedfor learning the structural information stemming from dyadicpath-tree signatures. Our experimental results on a widelyused benchmark dataset demonstrate comparable performanceto complex neural network based systems." @default.
- W2973161331 created "2019-09-19" @default.
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- W2973161331 date "2019-09-15" @default.
- W2973161331 modified "2023-10-16" @default.
- W2973161331 title "A Path Signature Approach for Speech Emotion Recognition" @default.
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- W2973161331 doi "https://doi.org/10.21437/interspeech.2019-2624" @default.
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