A New Approach for Speech Emotion Recognition using Single Layered Convolutional Neural Network

Vinoth Kumar, V; Palaiahnakote, Shivakumara; Khan, Surbhi Bhatia; Almusharraf, Ahlam

Abstract

Creating a computational device to identify human emotions via voice analysis represents a notable achievement in the sector of human-computer interaction, especially within the healthcare domain. We propose a new lightweight model for addressing challenges of emotions recognition. The model works based on CNN with change of kernel processing. The proposed model performs a direct matching to recognize speech emotions of different eight categories using a statistical model named Analysis of Variance (ANOVA) as kernel for features extraction and Cosine Similarity Measurement (CSM) as activation function for CNN model. This proposed model contains eight-folded single-layered intermediate neurons, and each neuron can segregate speech emotion pattern using CSM from the voice convergence matrix to explore a part of the solution from the whole solution. Experiment results demonstrates that the proposed model outperforms compared with multiple layered existing CNN methods in identifying the emotional state of a speaker.

Citation

Vinoth Kumar, V., Palaiahnakote, S., Khan, S. B., & Almusharraf, A. (in press). A New Approach for Speech Emotion Recognition using Single Layered Convolutional Neural Network. Malaysian journal of computer science,

• Journal Article Type: Article
• Acceptance Date: Mar 28, 2024
• Deposit Date: Mar 28, 2024
• Journal: Malaysian Journal of Computer Science
• Print ISSN: 0127-9084
• Peer Reviewed: Peer Reviewed
• Keywords: Analysis of Variance; Speech Emotion Recognition; Deep Learning; CNN; Cosine- similarity measurement