Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
By Christos-Nikolaos E. Anagnostopoulos, Theodoros Iliou, Ioannis Giannoukos. Artificial Intelligence Review, 2012
416 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing
View Paper: https://www.semanticscholar.org/paper/2d8f75f470c2859a002e6fa84acc7f1861e5e215
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
In Experimental and Cognitive Psychology
- Emily Mower Provost
- Zixing Zhang
- Jun Deng
- Björn Schuller
- Rui Xia
- Yang Liu
- Edward Jones
- Thamer Alhussain
In Signal Processing
- Emily Mower Provost
- Zixing Zhang
- Jun Deng
- Björn Schuller
- Rui Xia
- Yang Liu
- Edward Jones
- Thamer Alhussain
Citations in the corpus
- Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition (2014, background): “Index Terms— Transfer Learning, Cross-Corpus, SharedHidden-Layer Autoencoder, Emotion Recognition”
- Deep learning for robust feature generation in audiovisual emotion recognition (2013, background): “Emotion states do not have explicit temporal boundaries and emotion expression patterns often vary across individuals [7].”
- Survey on Emotional Body Gesture Recognition (2018, background): “sions or speech is encouraged to consult dedicated surveys [1], [13], [14].”
- Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, methodology): “Acoustic preprocessing such as denoising, as well as segmentation, is carried out to determine meaningful units of the signal [20].”