scholarly journals Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 66638-66646 ◽  
Author(s):  
Jheng-Long Wu ◽  
Yuanye He ◽  
Liang-Chih Yu ◽  
K. Robert Lai
Keyword(s):  
Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1579 ◽  
Author(s):  
Kyoung Ju Noh ◽  
Chi Yoon Jeong ◽  
Jiyoun Lim ◽  
Seungeun Chung ◽  
Gague Kim ◽  
...  

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.


2020 ◽  
Author(s):  
Bahar Azari ◽  
Christiana Westlin ◽  
Ajay Satpute ◽  
J. Benjamin Hutchinson ◽  
Philip A. Kragel ◽  
...  

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes- measuring the human brain, body, and subjective experience- and compare supervised classification studies with those from unsupervised clustering in which no a priori labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.


Author(s):  
Jingying Wang ◽  
Baobin Li ◽  
Changye Zhu ◽  
Shun Li ◽  
Tingshao Zhu

Automatic emotion recognition was of great value in many applications; however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Except face expression and voices, human gaits could reflect the walker's emotional state too. By utilizing 59 participants' gaits data with emotion labels, the authors train machine learning models that are able to “sense” individual emotion. Experimental results show these models work very well and prove that gait features are effective in characterizing and recognizing emotions.


PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237722
Author(s):  
Ana R. Delgado ◽  
Gerardo Prieto ◽  
Debora I. Burin
Keyword(s):  

2020 ◽  
Vol 12 (4) ◽  
pp. 255-258
Author(s):  
Ashley L. Ruba ◽  
Betty M. Repacholi

The process by which emotion concepts are learned is largely unexplored. Hoemann, Devlin, and Barrett (2020) and Shablack, Stein, and Lindquist (2020) argue that emotion concepts are learned through emotion labels (e.g., “happy”), which cohere variable aspects of emotions into abstract, conceptual categories. While such labeling-dependent learning mechanisms (supervised learning) are plausible, we argue that labeling-independent learning mechanisms (unsupervised learning) are also involved. Specifically, we argue that infants are uniquely situated to learn emotion concepts given their exceptional learning abilities. We provide evidence that children learn from complex, irregular input in other domains (e.g., symbolic numbers) without supervised instruction. Thus, while labels undoubtedly influence emotion concept learning, we must also look beyond language to create a comprehensive theory of emotion concept development.


2020 ◽  
Vol 12 (4) ◽  
pp. 251-253 ◽  
Author(s):  
Holly Shablack ◽  
Andrea G. Stein ◽  
Kristen A. Lindquist

Ruba and Repacholi (2020) review an important debate in the emotion development literature: whether infants can perceive and understand facial configurations as instances of discrete emotion categories. Consistent with a psychological constructionist account (Lindquist & Gendron, 2013; Shablack & Lindquist, 2019), they conclude that infants can perceive valence on faces, but argue the evidence is far from clear that infants perceive and understand discrete emotions. Ruba and Repacholi outline a novel developmental trajectory of emotion perception and understanding in which early emotion concept learning may be language-independent. In this comment, we argue that language may play a role in emotion concept acquisition even prior to children’s ability to produce emotion labels. We look forward to future research addressing this hypothesis.


2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Anton Batliner ◽  
Dino Seppi ◽  
Stefan Steidl ◽  
Björn Schuller

We deal with the topic of segmenting emotion-related (emotional/affective) episodes into adequate units for analysis and automatic processing/classification—a topic that has not been addressed adequately so far. We concentrate on speech and illustrate promising approaches by using a database with children's emotional speech. We argue in favour of the word as basic unit and map sequences of words on both syntactic and ‘‘emotionally consistent” chunks and report classification performances for an exhaustive modelling of our data by mapping word-based paralinguistic emotion labels onto three classes representing valence (positive, neutral, negative), and onto a fourth rest (garbage) class.


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