Speech Emotion Recognition through Machine Learning
As emotions play a vital role in communication, the detection and analysis of the same is of vital importance in today’s digital world of remote communication. Emotion detection is a challenging task because emotions are subjective. There is no common consensus on how to measure or categorize them. We define a SER system as a collection of methodologies that process and classify speech signals to detect emotions embedded in them. Such a system can find use in a wide variety of application areas like interactive voice based-assistant or caller-agent conversation analysis. The study attempts to detect underlying emotions in recorded speech by analysing the acoustic features of the audio data of recordings.