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About Sensors: Speech Emotion Recognition With Heterogeneous Feature Unification of Deep Neural Network
This document discusses a proposed deep neural network architecture for speech emotion recognition that aims to improve performance by unifying heterogeneous acoustic feature groups. The proposed approach extracts informative feature representations from different acoustic feature groups to eliminate redundant information. It then uses a fusion network to jointly learn discriminative acoustic features and classify emotions, achieving an accuracy of 64% on the IEMOCAP dataset. The key contributions are exploring how to make full use of low-level and high-level acoustic features and how deep learning can effectively fuse multiple feature representations for emotion recognition.
Detailed Information
| Author: | ['Mohammed Ansaf'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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