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PARKINSON’S TECHNIQUIES FOR ARECOGNISE SPEECH PROCESSING

K. Thamizhmaran

Abstract


Around the world, depression is a prevalent mental health issue that causes substantial disability. In addition to being widespread, it frequently co-occurs with other neurological and psychiatric disorders. The symptoms of Parkinson's disease (PD) immediately affect a person's capacity to operate. Treatment can benefit from early identification and detection of depression, but diagnosis usually involves a systematic diagnostic questionnaire or an interview with a healthcare professional. These patients' voice recordings were analyzed to extract paralinguistic elements, which were then fed into deep learning and machine learning methods to forecast depression. The findings are shown here, along with a discussion of the recordings' limitations due to their lack of linguistic content. Our models successfully classified depressed and non-depressed participants based on their vocal characteristics and the severity of their PD, with accuracies as high as 0.77. We discovered a correlation coefficient of 0.3936 between the severity of PD and depression, which is a useful feature for voice-based depression prediction. Our findings show a strong relationship between the severity of PD and feeling depressed. Voice could be a useful digital biomarker for PD patients to be screened for depression.


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References


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