Using Deep Learning to Recognize Emotions Through Speech Analysis

Arion Mitra, Ankita Biswas, Ananya Ghosh, Ahona Ghosh, Souptik Kumar Majumdar, Jayati Ghosh Dastidar

pp. 161–180, 2023.

---

Abstract

Emotion recognition is the identification of emotions usually through verbal communication and facial expressions such as happy, angry, sad, etc. Not only on the basis of a wide spectrum of moods, but different emotions can also be recognized in order to track mental health of as many people as possible for societal well being. Inside positive it detects specific emotions like happiness, satisfaction, or excitement -depending on how it’s configured. The main principles involved in the implementation of our sentiment recognition system that identifies various emotions: anger, happiness, depression, neutral, etc. are audio content and identification of the emotion associated with it. The application developed takes audio input, applies Mel-Frequency Cepstral Coefficients (MFCC) algorithm on it, compares them with those of the content of the existing audio file database depicting various human sentiments, and presents output in the text the emotion expressed by the user. The input from testing was gathered and meaningful spectral coefficients were extracted and stored in a database for comparison with future audio samples. The application extracts the coefficients of the external audio sample and matches it with those present in the database. MFCC algorithm is used to extract the spectral coefficients which are good and can be used for feature matching purposes discarding any static and background noise if present. We have done comparative analysis on our models for their performance evaluation, using four classification metrics and also presented the confusion matrix for better understanding.

---

Links

---

BibTeX