Speech Emotion Recognition Using CNN

By Zhengwei Huang, Ming Dong, Qi-rong Mao, Yongzhao Zhan. ACM Multimedia, 2014

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6060612f69d777760e0538b8bad28b4b7607745d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shiliang Zhang
2. Tiejun Huang
3. Wen Gao
4. Shiqing Zhang
5. Shuyong Gao
6. Yongzhao Zhan
7. Ming Dong
8. Edward Jones

In Experimental and Cognitive Psychology

1. Shiliang Zhang
2. Wen Gao
3. Shiqing Zhang
4. Tiejun Huang
5. Yongzhao Zhan
6. Ming Dong
7. Edward Jones
8. Thamer Alhussain

In Signal Processing

1. Shiliang Zhang
2. Wen Gao
3. Shiqing Zhang
4. Tiejun Huang
5. Yongzhao Zhan
6. Ming Dong
7. Edward Jones
8. Ron Hoory

Citations in the corpus

• Speech emotion recognition using convolutional and Recurrent Neural Networks (2016, methodology): “In particular, a CNNbased SER method has been proposed that learns salient features of SER using semi-CNNs [13].”
• Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms (2017, methodology): “Traditional methods were based on short-time frame-level feature extraction, followed by utterance-level information extraction, and classification or regression as required [3-8].”
• Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, background): “The preliminary version of this work was first presented in a shortened form as a conference abstract [14].”
• Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, methodology): “Accordingly, for the EMO-DB dataset and the eNTERFACE05 dataset, we test T ranges in [15, 30, 45, 64, 80, 100, 120], where T = 120 corresponds to about 1.”
• Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “Emotion recognition from speech using salient features has also been researched, where a CNN based on affective learning is used [121].”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Various ML-based and DL-based SER systems have been carried out on the basis of these extracted features for better analysis [162,163].”