Spoken Emotion Recognition Using Deep Learning

Albornoz, Enrique Marcelo; Sánchez-Gutiérrez, Máximo Eduardo; Licona, Fabiola Martínez; Rufiner, Hugo Leonardo; Goddard, John C.

doi:10.1007/978-3-319-12568-8_13

Cited by 21 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Deep Belief Networks (DBNs) improved emotion recognition, outperforming Deep Neural Networks [67] and Support Vector Machine [68]. It has been shown that two hidden layer DBNs are able to learn a new representation of audiovisual features, capturing complex non-linear dependencies between them [68].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

Muszyński

Tian

Lai

et al. 2021

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Recognizing emotional reactions of movie audiences to affective movie content is a challenging task in affective computing. Previous research on induced emotion recognition has mainly focused on using audiovisual movie content. Nevertheless, the relationship between the perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audiences (induced emotions) is unexplored. In this work, we studied the relationship between perceived and induced emotions of movie audiences. Moreover, we investigated multimodal modelling approaches to predict movie induced emotions from movie content based features, as well as physiological and behavioral reactions of movie audiences. To carry out analysis of induced and perceived emotions, we first extended an existing database for movie affect analysis by annotating perceived emotions in a crowd-sourced manner. We find that perceived and induced emotions are not always consistent with each other. In addition, we show that perceived emotions, movie dialogues, and aesthetic highlights are discriminative for movie induced emotion recognition besides spectators' physiological and behavioral reactions. Also, our experiments revealed that induced emotion recognition could benefit from including temporal information and performing multimodal fusion. Moreover, our work deeply investigated the gap between affective content analysis and induced emotion recognition by gaining insight into the relationships between aesthetic highlights, induced emotions, and perceived emotions.

show abstract

Section: Deep Belief Networkmentioning

confidence: 99%

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

Muszyński

Tian

Lai

et al. 2021

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

show abstract

“…Labmaster [4] proposed an automatic emotion recognition model using the combined algorithms namely DBN and RBM. In 2014, E.M. Albornoz et al [5], developed a model for spoken emotion recognition. They used the two techniques namely RBM and DBN and achieved an improvement of 8.67% over the existing baseline speaker independent scheme.…”

Section: Survey On Deep Boltzmann Machine (Dbm)mentioning

confidence: 99%

A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

Latha¹,

Priya²

2020

csit

View full text Add to dashboard Cite

Deep Learning is the recent machine learning technique that tries to model high level abstractions in databy using multiple processing layers with complex structures. It is also known as deep structured learning,hierarchical learning or deep machine learning. The term “deep learning" indicates the method used in trainingmulti-layered neural networks. Deep Learning technique has obtained remarkable success in the field of facerecognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotionsof humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), DeepBelief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This paperfocuses on the review of some of the deep learning techniques used by various researchers which paved the way toimprove the classification accuracy of the FEMG signals as well as the speech signals

show abstract

“…RBMs and DBNs are also used for parametric voice synthesis by (Zen and Senior 2014) and modeling statistical and probabilistic networks (Atwood et al 2014) among others. In speech emotion recognition, RBM and DBN achieved a significative performance improvement in comparison with other machine learning techniques (Albornoz et al 2014;S\' anchez-Guti\' errez et al 2014).…”

Section: Introductionmentioning

confidence: 98%

Post-training discriminative pruning for RBMs

et al. 2017

View full text Add to dashboard Cite

One of the major challenges in the area of artificial neural networks is the identification of a suitable architecture for a specific problem. Choosing an unsuitable topology can exponentially increase the training cost, and even hinder network convergence. On the other hand, recent research indicates that larger or deeper nets can map the problem features into a more appropriate space, and thereby improve the classification process, thus leading to an apparent dichotomy. In this regard, it is interesting to inquire whether independent measures, such as mutual information, could provide a clue to finding the most discriminative neurons in a network. In the present work we explore this question in the context of Restricted Boltzmann Machines, by employing different measures to realize posttraining pruning. The neurons which are determined by each measure to be the most discriminative, are combined and a classifier is applied to the ensuing network to determine its usefulness. We find that two measures in particular seem to be good indicators of the most discriminative neurons, producing savings of generally more than 50\% of the neurons, while maintaining an acceptable error rate. Further, it is borne out that start-M\' aximo S\' anchez-Guti\' errez \cdot John Goddard Close Departamento de Ingenier\' {\i} a El\' ectrica, Universidad Aut\' onoma Metropolitana -Iztapalapa (M\' exico).

show abstract

Spoken Emotion Recognition Using Deep Learning

Cited by 21 publications

References 21 publications

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

Recognizing Induced Emotions of Movie Audiences from Multimodal Information

A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

Post-training discriminative pruning for RBMs

Contact Info

Product

Resources

About