Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression

Leila Kerkeni (1, 2) , Youssef Serrestou (1) , Mohamed Mbarki (2) , Mohamed Mahjoub (2) , Kosai Raoof (1) , Catherine Cléder (3)

1 LAUM - Laboratoire d'Acoustique de l'Université du Mans
2 LATIS - Laboratory of Advanced Technology and Intelligent Systems
3 CREN - Centre de recherche en éducation de Nantes

Emotion recognition in spoken dialogues has been gaining increasing interest all through current years. A speech emotion recognition (SER) is a challenging research area in the field of Human Computer Interaction (HCI). It refers to the ability of detection the current emotional state of a human being from his or her voice. SER has potentially wide applications, such as the interface with robots, banking, call centers, car board systems, computer games etc. In our research we are interested to how, emotion recognition, can top enhance the quality of teaching for both of classroom orchestration and E-learnning. Integration of SER into aided teaching system, can guide teacher to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. In linguistic activity, from student's interaction and articulation, we can extract information about their emotional state. That is why learner's emotional state should be considered in the language classroom. In general, the SER is a computational task consisting of two major parts: feature extraction and emotion machine classification. The questions that arise here: What are the acoustic features needed for a most robust automatic recognition of a speaker's emotion? Which methods is most appropriate for classification? How the database used influence the recognition of emotion in speech?

Mots clés: Speech Emotion Recognition; Recurrent Neural Networks; SVM; Linear Regression; MFCC; Modulation Spectral Features

Domaines: Informatique et langage [cs.CL]; Intelligence artificielle [cs.AI]; Réseau de neurones [cs.NE]

Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Mohamed Mahjoub, Kosai Raoof, et al.. Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression. Alessandra Lintas; Stefano Rovetta; Paul F.M.J. Verschure; Alessandro E.P. Villa. Artificial Neural Networks and Machine Learning – ICANN 2017, 10613, Springer International Publishing, pp.451-453, 2019, Lecture Notes in Computer Science. ⟨hal-02432632⟩