Emotion recognition from speech: a review

By Shashidhar G. Koolagudi, K. S. Sekhara Rao. International Journal of Speech Technology, 2012

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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “variability, speech domain, noise recognition, and vocabulary volume or size needed or used in speech [102-105].”
Databases, features and classifiers for speech emotion recognition: a review (2018, background): “There are several features like local, global, continuous speech, qualitative, spectral and Teager Energy Operator (TEO)— based, excitation source features, vocal tract features in the pattern recognition problem reviewed in the literature by Koolagudi and Rao (2012a).”
Introducing AmuS: The Amused Speech Database (2017, background): “The emotive states are either considered as discrete classes [27,33] or as continuous values in a multidimensional space [1,31], or as dynamically changing processes over time.”
Automatic speech emotion recognition using recurrent neural networks with local attention (2017, methodology): “While a variety of both short-term and long-term features have been proposed [3], it is still unclear which features are more informative about emotions.”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, methodology): “The approach for speech emotion recognition (SER) primarily comprises two phases known as feature extraction and features classification phase [14].”