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About Speech Emotion Recognition With Deep Learning
This document summarizes a study that used deep learning to perform speech emotion recognition on a dataset containing German speech recordings labeled with three emotions: angry, neutral, and sad. The researchers used a deep neural network architecture with convolutional and fully connected layers to classify short segments of the audio recordings into one of the three emotion classes. The model achieved 96.97% accuracy on held-out test data, outperforming previous approaches on this dataset that used support vector machines with handcrafted audio features. This study demonstrates the effectiveness of an end-to-end deep learning approach for speech emotion recognition without requiring feature engineering.
Detailed Information
| Author: | ['Hari Haran'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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