Construction and Research of E-sports Speech Emotion Recognition Model

2022 ◽  
pp. 23-31
Author(s):  
Jason C. Hung ◽  
Jin-Che Chen
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Recognition Model

Speech emotion recognition model based on Bi-GRU and Focal Loss

2020 ◽  
Vol 140 ◽  
pp. 358-365
Author(s):  
Zijiang Zhu ◽  
Weihuang Dai ◽  
Yi Hu ◽  
Junshan Li
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Recognition Model ◽  
Model Based

Pattern recognition and features selection for speech emotion recognition model using deep learning

2020 ◽  
Vol 23 (4) ◽  
pp. 799-806
Author(s):  
Kittisak Jermsittiparsert ◽  
Abdurrahman Abdurrahman ◽  
Parinya Siriattakul ◽  
Ludmila A. Sundeeva ◽  
Wahidah Hashim ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Recognition Model ◽  
Selection For

scholarly journals A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission

2021 ◽  
Author(s):  
Lasse Hansen ◽  
Yan‐Ping Zhang ◽  
Detlef Wolf ◽  
Konstantinos Sechidis ◽  
Nicolai Ladegaard ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Recognition Model

scholarly journals A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission

2021 ◽  
Author(s):  
Lasse Hansen ◽  
Yan-Ping Zhang ◽  
Detlef Wolf ◽  
Konstantinos Sechidis ◽  
Nicolai Ladegaard ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Background Noise ◽  
Small Sample ◽  
Speech Emotion Recognition ◽  
First Episode ◽  
Control Group ◽  
Group Model ◽  
Healthy Controls ◽  
Voice Analysis ◽  
Recognition Model

Objective: Affective disorders have long been associated with atypical voice patterns, however, current work on automated voice analysis often suffers from small sample sizes and untested generalizability. This study investigated a generalizable approach to aid clinical evaluation of depression and remission from voice. Methods: A Mixture-of-Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora. We examined the model's predictive ability to classify the presence of depression on Danish speaking healthy controls (N = 42), patients with first-episode major depressive disorder (MDD) (N = 40), and the same patients in remission (N = 25) based on recorded clinical interviews. The model was evaluated on raw data, data cleaned for background noise, and speaker diarized data. Results: The model showed reliable separation between healthy controls and depressed patients at the first visit, obtaining an AUC of 0.71. Further, we observed a reliable treatment effect in the depression group, with speech from patients in remission being indistinguishable from that of the control group. Model predictions were stable throughout the interview, suggesting that as little as 20-30 seconds of speech is enough to accurately screen a patient. Background noise (but not speaker diarization) heavily impacted predictions, suggesting that a controlled environment and consistent preprocessing pipelines are crucial for correct characterizations. Conclusion: A generalizable speech emotion recognition model can effectively reveal changes in speaker depressive states before and after treatment in patients with MDD. Data collection settings and data cleaning are crucial when considering automated voice analysis for clinical purposes.

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