Emotion detection using average relative amplitude features through speech

Abstract

In this research work, a novel approach to emotion identification system is proposed for implementation in audio domain using human speech. In order to undertake the new approach, average relative bin frequency coefficients will be extracted from speech. In a noisy environment, audio data are not strictly aligned, thus getting proper noiseless signal is a challenge. Consequently, this affects the performance of emotion detection system. Due to these reasons, a newly proposed approach of Average Relative Bin Frequency technique in frequency domain will be implemented through audio data. Support vector machine with radial basis kernel will be used for the classification. Preliminary results showed an average of 86% accuracy for average relative frequency bin coefficients.Index Terms-Support vector machine, machine learning, relative frequency bin coefficients, information retrieval.

Challenges and Opportunities of Text-Based Emotion Detection: A Survey

“…SVM in machine learning is a supervised learning model with corresponding learning algorithms that examine data used for regression and classification analyses. A technique for automatically detecting emotions is implemented using SVM[162].• k-Neatest Neighbors (KNN): KNN is a technique for categorizing things based on training data nearest to the item. Problems involving classification and regression can be resolved using the KNN technique.…”

Speech Emotion Classification using Machine Learning

“…[6,13] AWS is a very simple approach in which each segment is assigned with a weight vector based on the type of emotion and the weights assigned are adjustable according to the input data. [6,13] …”