Abstract
As our knowledge, there is no dialog system for mental health-care domain in Hindi. This may be due to unavailability of user utterances corpora in Hindi for this domain. In this paper, we propose a novel algorithmic approach for user utterance generation in Hindi by considering dialects, linguistic attributes, symptoms, frequency of symptoms, and intensity of symptoms and history of symptoms. We use nine symptoms (anger, emptiness, fear, irritation, restlessness, suicide, sadness, tension, worry) as given in DSM5, ICD-11, and WHO guideline. These symptoms were used for generation of utterances and validation of the generated utterances for different type of mental diseases. We collected utterances by interviewing patients in clinic and found that it closely match to the utterance generated by proposed algorithm. The generated utterance corpus is also validated using machine learning methods in the framework of CNN, Bi-LSTM and Dense.
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Prakash, S., Singh, M.K., Tiwary, U.S., Srivastava, M. (2024). HUCMD: Hindi Utterance Corpus for Mental Disorders. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_5
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