Teager Mel and PLP Fusion Feature Based Speech Emotion Recognition

2015 ◽  
Author(s):  
Xiao Chen ◽  
Haifeng Li ◽  
Lin Ma ◽  
Xinlei Liu ◽  
Jing Chen
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Feature Based ◽  
Fusion Feature

scholarly journals Fusion-ConvBERT: Parallel Convolution and BERT Fusion for Speech Emotion Recognition

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6688
Author(s):  
Sanghyun Lee ◽  
David K. Han ◽  
Hanseok Ko
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Emotional State ◽  
Speech Emotion Recognition ◽  
Human Computer Interactions ◽  
Fusion Model ◽  
Additional Element ◽  
Proposed Model ◽  
Feature Based ◽  
Available Information

Speech emotion recognition predicts the emotional state of a speaker based on the person’s speech. It brings an additional element for creating more natural human–computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the advent of deep learning, there have been some efforts in applying the deep-network-based approach to the problem of emotion recognition. As deep learning automatically extracts salient features correlated to speaker emotion, it brings certain advantages over the handcrafted-feature-based methods. There are, however, some challenges in applying them to the emotion recognition problem, because data required for properly training deep networks are often lacking. Therefore, there is a need for a new deep-learning-based approach which can exploit available information from given speech signals to the maximum extent possible. Our proposed method, called “Fusion-ConvBERT”, is a parallel fusion model consisting of bidirectional encoder representations from transformers and convolutional neural networks. Extensive experiments were conducted on the proposed model using the EMO-DB and Interactive Emotional Dyadic Motion Capture Database emotion corpus, and it was shown that the proposed method outperformed state-of-the-art techniques in most of the test configurations.

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.

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