Semi-Supervised Auto encoders for Speech Emotion Recognition

Emotion recognition stands as a pivotal facet of human-computer interaction, finding applications across affective computing, healthcare, and beyond. This paper offers an exhaustive examination of emotion recognition techniques implemented using Python. It delves into diverse methodologies employed for emotion recognition, spanning machine learning algorithms, deep learning models, and hybrid approaches. Moreover, it explores commonly utilized datasets for training and evaluation, alongside performance metrics for assessing technique effectiveness. Additionally, the paper highlights existing challenges and outlines future directions in the field, paving the path for further advancements.