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Home > Archives > Volume 20, No 14 (2022) > Article

DOI: 10.48047/nq.2022.20.14.NQ88010

A Survey Of Machine Learning Techniques OnSpeech Based Emotion Recognition And Post Traumatic Stress DisorderDetection

Chappidi Suneetha, Dr. Raju Anitha


This paper reviews the literature on a wide array of methodologies utilized for emotion recognition from stressed speech. These techniques include models for the detection of post-traumatic stress disorder (PTSD), neural networks, and long and short-term memory networks (LSTM). The relevance of selecting alternative classification models has also been emphasized. The crucial issues to consider for future emotion recognition research in general, specifically in the Indian context, have been highlighted where applicable. Finally, possible future trends in stressed emotion recognition are discussed, including the use of region based convolutional neural network (RCNN), you look only once (YOLO),recurrent neural networks (RNN), and LSTM techniques to solve new challenges in this domain.


stressed emotion recognition, CNN, YOLO, RNN, LSTM

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