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- W4367011806 abstract "Speech emotion recognition (SER) is a popular area of research, and its presence has been observed in various sectors including the smart healthcare system. An SER-enabled smart health system may facilitate the medical practitioners to improve the diagnosis process, by incorporating patient mental health. This is particularly vital in a pandemic/adverse situation when people especially elders are being forced to take psychological counseling/medical advice online. It has been observed that during the counseling stage, patients normally preferred the mother language for communication as they can communicate their problems more easily. However, it has been observed that most of the researchers tried to perceive patient mental health by employing non-regional language audio datasets such as English. Very little work has been done to perceive patient mental health from regional languages such as ‘Bangla’. Thus, in the proposed work, an attempt has been made to facilitate the diagnosis process by incorporating patient mental health (‘angry’, ‘fear’, ‘happy’, ‘sad’, and ‘neutral’) using widely used ML algorithms. To achieve the objective, important mel-frequency cepstral coefficients (MFCCs) are selected using the explainable AI approach. Here, the random forest (RF) algorithm has been used for the said purpose. SUST Bangla Emotional Speech Corpus (SUBESCO) is used as the training dataset. Experimental result shows the use of important MFCCs gives better accuracy compared to the conventional approach where the first 26 or 13 MFCCs are mostly employed. Comparative results analysis also shows that among the employed ML algorithms 1D CNN has shown better performance. Thus, in the proposed work, 1D CNN-based smart health system has been adopted." @default.
- W4367011806 created "2023-04-27" @default.
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- W4367011806 date "2023-01-01" @default.
- W4367011806 modified "2023-09-24" @default.
- W4367011806 title "A Smart System for Assessment of Mental Health Using Explainable AI Approach" @default.
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- W4367011806 doi "https://doi.org/10.1007/978-981-19-5191-6_21" @default.
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