Year: 2020
Type: article
Abstract: The aim of this project work is to propose a speech emotion recognition method based on speech features and speech transcriptions (text). Modelling emotional behaviours is a challenging task due to the variability in perceiving and describing emotions. We try to perform emotion analysis on the speech by collecting speech and textual features and applying a deep neural network model which can classify the sentiments of the speech. Ideally, we would like to experiment with several deep neural network models which take in different combinations of speech features and text as inputs. Speech features such as Mel-Frequency Cepstral Coefficients (MFCC) help retain emotion related low-level characteristics in speech whereas text helps capture the semantic meaning, both of which help in different aspects of emotion detection.
Author: Balaji Dharamsoth
Institution: Chaitanya Bharathi Institute of Technology
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W3038653086