Speech emotion recognition with unsupervised feature learning

2015 ◽  
Vol 16 (5) ◽  
pp. 358-366 ◽  
Author(s):  
Zheng-wei Huang ◽  
Wen-tao Xue ◽  
Qi-rong Mao
Keyword(s):  
Emotion Recognition ◽  
Feature Learning ◽  
Speech Emotion Recognition ◽  
Unsupervised Feature Learning

scholarly journals Unsupervised Feature Learning for Speech Emotion Recognition Based on Autoencoder

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2086
Author(s):  
Yangwei Ying ◽  
Yuanwu Tu ◽  
Hong Zhou
Keyword(s):  
Emotion Recognition ◽  
Data Augmentation ◽  
Feature Learning ◽  
Human Potential ◽  
Speech Emotion Recognition ◽  
Unsupervised Feature Learning ◽  
Learning Techniques ◽  
Speech Data ◽  
Data Division ◽  
Speech Features

Speech signals contain abundant information on personal emotions, which plays an important part in the representation of human potential characteristics and expressions. However, the deficiency of emotion speech data affects the development of speech emotion recognition (SER), which also limits the promotion of recognition accuracy. Currently, the most effective approach is to make use of unsupervised feature learning techniques to extract speech features from available speech data and generate emotion classifiers with these features. In this paper, we proposed to implement autoencoders such as a denoising autoencoder (DAE) and an adversarial autoencoder (AAE) to extract the features from LibriSpeech for model pre-training, and then conducted experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets for classification. Considering the imbalance of data distribution in IEMOCAP, we developed a novel data augmentation approach to optimize the overlap shift between consecutive segments and redesigned the data division. The best classification accuracy reached 78.67% (weighted accuracy, WA) and 76.89% (unweighted accuracy, UA) with AAE. Compared with state-of-the-art results to our knowledge (76.18% of WA and 76.36% of UA with the supervised learning method), we achieved a slight advantage. This suggests that using unsupervised learning benefits the development of SER and provides a new approach to eliminate the problem of data scarcity.

scholarly journals Deep Cross-Corpus Speech Emotion Recognition: Recent Advances and Perspectives

2021 ◽  
Vol 15 ◽  
Author(s):  
Shiqing Zhang ◽  
Ruixin Liu ◽  
Xin Tao ◽  
Xiaoming Zhao
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Feature Learning ◽  
Learning Ability ◽  
Speech Emotion Recognition ◽  
Practical Applications ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Comprehensive Survey ◽  
Cross Language

Automatic speech emotion recognition (SER) is a challenging component of human-computer interaction (HCI). Existing literatures mainly focus on evaluating the SER performance by means of training and testing on a single corpus with a single language setting. However, in many practical applications, there are great differences between the training corpus and testing corpus. Due to the diversity of different speech emotional corpus or languages, most previous SER methods do not perform well when applied in real-world cross-corpus or cross-language scenarios. Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have increasingly been adopted for cross-corpus SER. This paper aims to provide an up-to-date and comprehensive survey of cross-corpus SER, especially for various deep learning techniques associated with supervised, unsupervised and semi-supervised learning in this area. In addition, this paper also highlights different challenges and opportunities on cross-corpus SER tasks, and points out its future trends.

scholarly journals Deep Residual Local Feature Learning for Speech Emotion Recognition

2020 ◽  
pp. 241-252
Author(s):  
Sattaya Singkul ◽  
Thakorn Chatchaisathaporn ◽  
Boontawee Suntisrivaraporn ◽  
Kuntpong Woraratpanya
Keyword(s):  
Emotion Recognition ◽  
Feature Learning ◽  
Local Feature ◽  
Speech Emotion Recognition

Unsupervised Feature Learning for EEG-based Emotion Recognition

2017 ◽  
Author(s):  
Zirui Lan ◽  
Olga Sourina ◽  
Lipo Wang ◽  
Reinhold Scherer ◽  
Gernot Muller-Putz
Keyword(s):  
Emotion Recognition ◽  
Feature Learning ◽  
Unsupervised Feature Learning

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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