A novel stochastic deep conviction network for emotion recognition in speech signal

Article type: Research Article

Authors: Shukla, Shilpi^{a; *} | Jain, Madhu^b

Affiliations: [a] Assistant Professor, Mahatma Gandhi Mission’s College of Engineering and Technology, Noida, Uttar Pradesh, India | [b] Associate Professor, Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, (Uttar Pradesh), India

Correspondence: [*] Corresponding author. Ms Shilpi Shukla, Assistant Professor, Mahatma Gandhi Mission’s College of Engineering and Technology, Noida, Uttar Pradesh, India. E-mail: [email protected].

Abstract: Deep learning is far and wide considered to be the most powerful method in computer vision fields, which has a lot of applications such as image recognition, robot navigation systems, and self-driving cars. Recent developments in neural networks have led to an efficient end-to-end architecture to human activity representation and classification. In light of these recent events in deep learning, there is now much considerable concern about developing less expensive computation and memory-wise methods. This paper presents an optimized end-to-end approach named stochastic deep conviction network (SDCN) formulated using the deep learning method. It comprises of deep learning method namely deep belief network (DBN), two supervised machine learning algorithm support vector machine (SVM) and decision tree (DT) with optimization capability for speech emotion identification. In the beginning, pre-processing is performed and the features are automatically extracted from the input speech signal by the DBN. Since speech signal features loses most of the information and the performance cannot be guaranteed because dynamic interactions can generate uncountable emotion-specific experiences that have the same core feeling state but different perceptual inclinations so DBN provides more robust features. The next step is to classify the emotions in the training phase; here the SVM classifier is chosen which performs dual classification. In order to enhance this classification process, defects must be reduced and the best discrimination of the extracted features should be obtained hence particle swarm optimization (PSO) technique is being added along with SVM classifier in the training phase. To reduce the over fitting problem and risks of a single classifier a DT is being used in the testing phase for the exact identification of emotions (anger, disgust, fear, happiness, neutral and sadness) and therefore it obtains better performance than a single classifier. The complication of the decision tool is that it can increase the computation time. Thus to eliminate this defect whale optimization (WO) technique is being added to the decision tree to reduce the complexity of the system, which in turn lessens the time taken for recognizing the emotion of the speech signal. This formulated proposed SDCN system improves the recognition rate accurately. In this work, theMATLAB environment is being preferred to perform speech emotion recognition. Using the proposed technique the achieved accuracy of emotion detection is above 95% and the identification of various emotions exceeds 98% recognition rate with a computation time of 23 seconds, which has not been achieved so far by any other existing techniques.

Keywords: Stochastic deep conviction network, restricted Boltzmann machine, particle swarm optimization, support vector machine, whale optimization

DOI: 10.3233/JIFS-191753

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5175-5190, 2020