Verified Laboratory MATLAB Implementation

Speech Emotion Recognition using Deep Learning Matlab

Explore Speech Emotion Recognition (SER) using Deep Learning in Matlab. Learn techniques for building effective HCI systems. Uncover challenges & solutions. ...

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MATLAB Laboratory Run Demonstration 100% Tested

Speech Emotion Recognition using Deep Learning Matlab

Environment: MATLAB R2024b / R2026 Compatible

Deliverables: .m scripts, .slx models, .mat data

Validation: Verified simulation plots & GUI App

Documentation: Full IEEE formatted project report

Project Methodology & Algorithm Details

ABSTRACT

Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.

INTRODUCTION

E MOTION recognition from speech has evolved from being a niche to an important component for HumanComputer Interaction (HCI). These systems aim to facilitate the natural interaction with machines by direct voice interaction instead of using traditional devices as input to understand verbal content and make it easy for human listeners to react. Some applications include dialogue systems for spoken languages such as call center conversations, onboard vehicle driving system and utilization of emotion patterns from the speech in medical applications. Nonetheless, there are many problems in HCI systems that still need to be properly addressed, particularly as these systems move from lab testing to real-world application. Hence, efforts are required to effectively solve such problems and achieve better emotion recognition by machines. Determining the emotional state of humans is an idiosyncratic task and may be used as a standard for any emotion recognition model. Amongst the numerous models used for categorization of these emotions, a discrete emotional approach is considered as one of the fundamental approaches. It uses various emotions such as anger, boredom, disgust, surprise, fear, joy, happiness, neutral and sadness. Another important model that is used is a three-dimensional continuous space with parameters such as arousal, valence, and potency.

The approach for speech emotion recognition (SER) primarily comprises two phases known as feature extraction and features classification phase. In the field of speech processing, researchers have derived several features such as source-based excitation features, prosodic features, vocal traction factors, and other hybrid features. The second phase includes feature classification using linear and nonlinear classifiers. The most commonly used linear classifiers for emotion recognition include Bayesian Networks (BN) or the Maximum Likelihood Principle (MLP) and Support Vector Machine (SVM). Usually, the speech signal is considered to be non-stationary. Hence, it is considered that non-linear classifiers work effectively for SER.