Emotion Classification from Noisy Speech - A Deep Learning Approach

TL;DR

This paper explores the use of deep learning techniques to classify emotions from noisy speech signals, evaluating accuracy and discussing future improvements for robustness.

Contribution

It presents an analysis of deep learning methods' effectiveness in emotion recognition from noisy speech and suggests directions for enhancing robustness.

Findings

Deep learning achieves promising accuracy in noisy conditions

Noise significantly impacts emotion classification performance

Future work needed for improved robustness

Abstract

This paper investigates the performance of Deep Learning for speech emotion classification when the speech is compounded with noise. It reports on the classification accuracy and concludes with the future directions for achieving greater robustness for emotion recognition from noisy speech.