Scientists at Jinhua Advanced Research Institute and Harbin University of Science and Technology have developed a deep-learning algorithm that can identify depression through speech. This algorithm was trained to recognize emotions in spoken language by looking at various pertinent traits.
Millions of people all over the world go through depression, which is a common mental health disorder. Depression can significantly impair daily functioning if it goes untreated, and it also raises the risk of suicide and self-harm. The prognosis and quality of life for those who are depressed can be significantly improved by early detection and treatment. Deep learning and artificial intelligence developments in recent years have created new opportunities for creating tools that can recognize depression in speech.
A multi-information joint decision algorithm model published in Mobile Networks and Applications is established using emotion recognition. In order to determine whether or not the subjects have depression, the model is applied to the subjects’ representative data.
The DAIC-WOZ dataset is used as the primary source of speech data in this study, which carefully examines the use of speech data to diagnose depression. The DAIC-WOZ database contains audio and 3D facial expressions of people with and without depressive disorders. A virtual representative asked the interviewee several questions about their health and personal situation while audio recordings and facial expressions were being taken.
The researchers’ primary goal was to examine the speech patterns of people who have depression and to figure out whether it is possible to use speech data to assist in the diagnosis of this disorder.
How deep learning is used to detect depression from speech
Deep learning is used in a number of stages to identify depression in speech. Preprocessing is the first step, and it entails actions like emphasizing the speech signal, breaking it up into frames, identifying endpoints, and reducing noise.
The next step is to extract pertinent features from the speech signals using the open-source program OpenSmile. The most relevant features are chosen after thoroughly examining these characteristics and their influence on depression diagnosis. Principal component analysis was used to limit the number of features to simplify the data. The final step is to train and test a convolutional neural network, which yields an accuracy of 87% for depression diagnosis based solely on speech analysis.
Advantages of using speech for depression detection
- Speech can reveal information about a person’s emotions and thought patterns, which are crucial signs of depression.
- Speech analysis is more accessible to people who might not have access to conventional clinical evaluations because it is non-invasive and can be carried out remotely.
- Real-time speech analysis can give people and medical professionals immediate feedback.
Conclusion
The detection and treatment of depression may be revolutionized by deep learning. A deep learning model developed in the study for depression diagnosis can offer a non-invasive and accessible way to detect depression using speech analysis, which can help people with this condition live better lives. This research could pave the way for the development of similar AI tools that can analyze speech to find signs of psychiatric disorders.
Article Source: Reference Paper | Reference Article
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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.
