Using AI to detect depression in our voice
This technique can help diagnose depression quickly. This work, which was documented in a recently published paper, used standard audio recording sets to develop a method that uses a range of machine-learning algorithms to provide a more accurate diagnosis than traditional approaches.
The study, built on previous research, highlights the key role that the movement of our voice can play in helping us identify our mood. The team hopes they can eventually develop a commercial app to help users self-diagnose.
“Using an app to collect voice samples when people speak naturally in a real scenario. The app, which runs on the user's phone, detects and tracks moods such
as depression over time. Just as you have a step counter on your phone, you may have a depression indicator based on your voice when you use the phone. " With depression affecting 15% of Indians at some point in their lives, the group believes that such a tool is important to help people better reflect on their moods over time and gain a better understanding of their psychological well-being. Active work It joins several interesting projects that work along the same lines. 18 months or so ago I wrote about some interesting new research that could accurately predict depression without hearing our throats. While it is fair to say that such work has not yet come to market, a recent study from the University of Vermont highlights the work still being done in this field. This reveals the utility of machine learning to detect signs of depression or anxiety in one in 20% of young children who believe they are suffering from some form of anxiety or depression. It is difficult to diagnose the condition in young children because they are not able to reliably express the feelings they have, which leads to many children being left undiagnosed and therefore untreated.
As with most conditions, the earlier you diagnose mental health problems in children, the more likely it is that their brains are still developing, especially in young children, and untreated conditions can easily develop later in life, such as drug abuse. The traditional method of diagnosis involves a semi-structured interview with a physician, but researchers believe that AI services can do better in terms of accuracy and speed.
Researchers have developed a modified version of the Trier-Social Stress Task, which is designed to evoke feelings of stress and / or anxiety in a subject. This involves asking a group of volunteer children to improvise a short story, judging by how interesting each story is. The judge was ordered to give a neutral or
negative speech while holding a strict expression throughout the speech. In addition to testing, each child was also given a structured clinical interview to test for various problems.
A machine-learning algorithm was used to analyze the different statistical characteristics of each story's audio recordings in relation to the child's diagnosis. Researchers have found that this algorithm has been successful in identifying children, the middle point of the story being the most valuable part in terms of attendance ability. Researchers explain,
"The algorithm is able to detect children with an internal disorder diagnosed with 80% accuracy, and in most cases better match the accuracy of the parent's checklist." The team plans to further develop the system as a screening tool that can potentially be used in the clinical environment through the app. It can also be combined with motion analysis or other diagnostic tools to better diagnose children who are depressed and anxious, and the team is confident that they can provide a diagnosis before their parents even suspect anything is wrong.
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