Role of gender influence in vocal Hindi conversations: A study on speech emotion recognition

2016 ◽  
Author(s):  
Devika Verma ◽  
Debajyoti Mukhopadhyay ◽  
Emmanuel Mark
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Gender Influence

Speech Emotion Recognition Based on Gender Influence in Emotional Expression

2019 ◽  
Vol 15 (4) ◽  
pp. 22-40
Author(s):  
P Vasuki ◽  
Divya Bharati R
Keyword(s):  
Emotion Recognition ◽  
Emotional Expression ◽  
Speech Emotion Recognition ◽  
Gender Influence ◽  
Gender And Age ◽  
Gender Based ◽  
Mixed Gender ◽  
Real Challenge ◽  
Gender Specific ◽  
Optimal Feature

The real challenge in human-computer interaction is understanding human emotions by machines and responding to it accordingly. Emotion varies by gender and age of the speaker, location, and cause. This article focuses on the improvement of emotion recognition (ER) from speech using gender-biased influences in emotional expression. The problem is addressed by testing emotional speech with an appropriate specific-gender ER system. As acoustical characteristics vary among the genders, there may not be a common optimal feature set across both genders. Gender-based speech emotion recognition, a two-level hierarchical ER system is proposed, where the first level is gender identification which identifies the gender, and the second level is a gender-specific ER system, trained with an optimal feature set of expressions of a particular gender. The proposed system increases the accuracy of traditional Speech Emotion Recognition Systems (SER) by 10.36% than the SER trained with mixed gender training when tested on the EMO-DB Corpus.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Sign in / Sign up

Export Citation Format

Share Document