CAFC: a convolutional neural approach for sentiment recognition in speech signals

Abstract

Sentiment analysis using speech is an emerging field that leverages vocal cues such as pitch, tone, and rhythm to capture human emotions beyond textual content. This paper shows audio-based sentiment classification using Mel-Frequency Cepstral Coefficients (MFCCs) and compares the performance of traditional machine learning (ML) and deep learning (DL) models introduced in this study. A novel Convolutional Acoustic-Feature Classifier (CAFC) is proposed to address the limitations of existing approaches which is based on a 1D Convolutional Neural Network (CNN) architecture optimized for sequential acoustic features. The proposed CAFC model can automatically capture temporal and spectral dependence relationships in speech, and it is more efficient and accurate compared with current supervised methods in audio sentiment analysis. The CAFC model was validated using 5-fold cross-validation which demonstrated that CAFC not only consistently outperforms all baseline models but also achieving an accuracy of 96%, with superior precision, recall, and F1-score. For sentiment recognition in speech the findings indicated the effectiveness of CNN-based architectures and underscore the importance of leveraging acoustic features for robust human-computer interaction systems.