Deep network for emotion recognition

Yueqi Liu

doi:10.1117/12.3003858

11 October 2023 Deep network for emotion recognition

Yueqi Liu

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002X (2023) https://doi.org/10.1117/12.3003858
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Speech recognition is an Al-enhanced technology converting the human voice from analogue to digital form to be further processed. Speech recognition can be applied to multiple fields including banking, health care as well as all the commonly used activated digital assistants such as Siri, Alexa, and Cortana. Recently, with the development of deep learning, a higher level of speech recognition. In this research, I am going to explore deep learning's function in communication- or more specifically, in emotion recognition in the context of Alzheimer's diseases. The key to dealing with the problem is based on finding an objective method to normalize emotion, match each emotion with the correct label and test the model with unstudied data samples. Characterized sound data are processed by extracting MFCC value and then trained with a CNN-based model. The algorithm is then applied to new data to test performance. Models are evaluated by comparing the result of each of the ten emotions evolved. Our result shows that female-angry has the highest recognition accuracy while male-fearful has the lowest. Later, simple sentences on different topics will be added as a package that can be called flexibly according to the need of various users and also as a standby in a situation when detecting negative emotions. This paper provides a detailed explanation of the app developed for Alzheimer's patients which are characterized by its ability to recognize emotions and make corresponding reactions. I construct the architecture, examine the strengths and challenges of the model, report performance and test its function in real-world situations.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yueqi Liu "Deep network for emotion recognition", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002X (11 October 2023); https://doi.org/10.1117/12.3003858

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KEYWORDS

Emotion

Data modeling

Machine learning

Data processing

Deep learning

Alzheimer disease

Education and training

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