Speech Emotion Recognition Using Local and Global Features

2017 ◽  
pp. 3-13 ◽  
Author(s):  
Yuanbo Gao ◽  
Baobin Li ◽  
Ning Wang ◽  
Tingshao Zhu
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Global Features ◽  
Local And Global Features

scholarly journals A Method upon Deep Learning for Speech Emotion Recognition

2021 ◽  
Vol 4 (4) ◽  
pp. 273
Author(s):  
Nhat Truong Pham ◽  
Ngoc Minh Duc Dang ◽  
Sy Dung Nguyen
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Learning Approaches ◽  
Global Features ◽  
Time Step ◽  
Creative Commons ◽  
Computational Resources ◽  
Deep Learning Model

Feature extraction and emotional classification are significant roles in speech emotion recognition. It is hard to extract and select the optimal features, researchers can not be sure what the features should be. With deep learning approaches, features could be extracted by using hierarchical abstraction layers, but it requires high computational resources and a large number of data. In this article, we choose static, differential, and acceleration coefficients of log Mel-spectrogram as inputs for the deep learning model. To avoid performance degradation, we also add a skip connection with dilated convolution network integration. All representatives are fed into a self-attention mechanism with bidirectional recurrent neural networks to learn long term global features and exploit context for each time step. Finally, we investigate contrastive center loss with softmax loss as loss function to improve the accuracy of emotion recognition. For validating robustness and effectiveness, we tested the proposed method on the Emo-DB and ERC2019 datasets. Experimental results show that the performance of the proposed method is strongly comparable with the existing state-of-the-art methods on the Emo-DB and ERC2019 with 88% and 67%, respectively. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

scholarly journals An Ensemble Model for Multi-Level Speech Emotion Recognition

2019 ◽  
Vol 10 (1) ◽  
pp. 205 ◽  
Author(s):  
Chunjun Zheng ◽  
Chunli Wang ◽  
Ning Jia
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Ensemble Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Local Features ◽  
Speech Emotion Recognition ◽  
Model Design ◽  
Local Data ◽  
Global Features

Speech emotion recognition is a challenging and widely examined research topic in the field of speech processing. The accuracy of existing models in speech emotion recognition tasks is not high, and the generalization ability is not strong. Since the feature set and model design of effective speech directly affect the accuracy of speech emotion recognition, research on features and models is important. Because emotional expression is often correlated with the global features, local features, and model design of speech, it is often difficult to find a universal solution for effective speech emotion recognition. Based on this, the main research purpose of this paper is to generate general emotion features in speech signals from different angles, and use the ensemble learning model to perform emotion recognition tasks. It is divided into the following aspects: (1) Three expert roles of speech emotion recognition are designed. Expert 1 focuses on three-dimensional feature extraction of local signals; expert 2 focuses on extraction of comprehensive information in local data; and expert 3 emphasizes global features: acoustic feature descriptors (low-level descriptors (LLDs)), high-level statistics functionals (HSFs), and local features and their timing relationships. A single-/multiple-level deep learning model that meets expert characteristics is designed for each expert, including convolutional neural network (CNN), bi-directional long short-term memory (BLSTM), and gated recurrent unit (GRU). Convolutional recurrent neural network (CRNN), based on a combination of an attention mechanism, is used for internal training of experts. (2) By designing an ensemble learning model, each expert can play to its own advantages and evaluate speech emotions from different focuses. (3) Through experiments, the performance of various experts and ensemble learning models in emotion recognition is compared in the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus and the validity of the proposed model is verified.

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.

Interaction and Transition Model for Speech Emotion Recognition in Dialogue

2017 ◽  
Author(s):  
Ruo Zhang ◽  
Ando Atsushi ◽  
Satoshi Kobashikawa ◽  
Yushi Aono
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Transition Model
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