Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks

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Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “action instead of using traditional devices as input to understand verbal content and make it easy for human listeners to react [4]–[6].”
A deep learning approach for credit scoring using credit default swaps (2017, background): “…with success in classification task, e.g., computer vision (Krizhevsky et al., 2012), health state classification (Tamilselvan and Wang, 2013; Abdel-Zaher and Eldeib, 2016), speech and language processing (Mohamed et al., 2012; Ling et al., 2013) and emotion recognition (Le and Provost, 2013).”
Speech Emotion Recognition Using CNN (2014, background): “For SER, while many previous works about deep neural network focused on learning discriminative features [4, 5] from input, but did not consider to disentangle factors of variation.”
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, methodology): “More recently, Le et al. [17] investigated dynamic frame-level modeling with hybrid DBN-HMM classifiers on the FAU Aibo spontaneous emotion corpus [35] and achieved state-of-the-art results on the 5-class problem.”