Human Vocal Sentiment Analysis

Abstract

In this paper, we use several techniques with conventional vocal feature extraction (MFCC, STFT), along with deep-learning approaches such as CNN, and also context-level analysis, by providing the textual data, and combining different approaches for improved emotion-level classification. We explore models that have not been tested to gauge the difference in performance and accuracy. We apply hyperparameter sweeps and data augmentation to improve performance. Finally, we see if a real-time approach is feasible, and can be readily integrated into existing systems.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyhuang2019human

Related papers

• Objective Human Affective Vocal Expression Detection And Automatic Classification With Stochastic Models And Learning Systems (2019)0.00
• Emotion Recognition From Speech (2019)0.00
• A Study Of Language And Classifier-independent Feature Analysis For Vocal Emotion Recognition (2018)0.00
• Self-relation Attention And Temporal Awareness For Emotion Recognition Via Vocal Burst (2022)4.18
• An Ensemble Framework Of Voice-based Emotion Recognition System For Films And TV Programs (2018)9.41
• Automatically Augmenting An Emotion Dataset Improves Classification Using Audio (2018)0.00
• Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection (2019)0.00
• Self-supervision And Learnable Strfs For Age, Emotion, And Country Prediction (2022)0.00