A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.
Around one in five children suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.
“We need quick, objective tests to catch kids when they are suffering,” says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center’s Vermont Center for Children, Youth and Families and lead author of the study. “The majority of kids under eight are undiagnosed.”
Early diagnosis is critical because children respond well to treatment while their brains are still developing, but if they are left untreated they are at greater risk of substance abuse and suicide later in life. Standard diagnosis involves a 60-90 minute semi-structured interview with a trained clinician and their primary care-giver. McGinnis, along with University of Vermont biomedical engineer and study senior author Ryan McGinnis, has been looking for ways to use artificial intelligence and machine learning to make diagnosis faster and more reliable.
The researchers used an adapted version of a mood induction task called the Trier-Social Stress Task, which is intended to cause feelings of stress and anxiety in the subject. A group of 71 children between the ages of three and eight were asked to improvise a three-minute story, and told that they would be judged based on how interesting it was. The researcher acting as the judge remained stern throughout the speech, and gave only neutral or negative feedback. After 90 seconds, and again with 30 seconds left, a buzzer would sound and the judge would tell them how much time was left.
“The task is designed to be stressful, and to put them in the mindset that someone was judging them,” says Ellen McGinnis.
The children were also diagnosed using a structured clinical interview and parent questionnaire, both well-established ways of identifying internalizing disorders in children.
The researchers used a machine learning algorithm to analyze statistical features of the audio recordings of each kid’s story and relate them to the child’s diagnosis. They found the algorithm was highly successful at diagnosing children, and that the middle phase of the recordings, between the two buzzers, was the most predictive of a diagnosis.
“The algorithm was able to identify children with a diagnosis of an internalizing disorder with 80 percent accuracy, and in most cases that compared really well to the accuracy of the parent checklist,” says Ryan McGinnis. It can also give the results much more quickly – the algorithm requires just a few seconds of processing time once the task is complete to provide a diagnosis.
The algorithm identified eight different audio features of the children’s speech, but three in particular stood out as highly indicative of internalizing disorders: low-pitched voices, with repeatable speech inflections and content, and a higher-pitched response to the surprising buzzer. Ellen McGinnis says these features fit well with what you might expect from someone suffering from depression. “A low-pitched voice and repeatable speech elements mirrors what we think about when we think about depression: speaking in a monotone voice, repeating what you’re saying,” says Ellen McGinnis.
The higher-pitched response to the buzzer is also similar to the response the researchers found in their previous work, where children with internalizing disorders were found to exhibit a larger turning-away response from a fearful stimulus in a fear induction task.
The voice analysis has a similar accuracy in diagnosis to the motion analysis in that earlier work, but Ryan McGinnis thinks it would be much easier to use in a clinical setting. The fear task requires a darkened room, toy snake, motion sensors attached to the child and a guide, while the voice task only needs a judge, a way to record speech and a buzzer to interrupt. “This would be more feasible to deploy,” he says.
Ellen McGinnis says the next step will be to develop the speech analysis algorithm into a universal screening tool for clinical use, perhaps via a smartphone app that could record and analyze results immediately. The voice analysis could also be combined with the motion analysis into a battery of technology-assisted diagnostic tools to help identify children at risk of anxiety and depression before even their parents suspect that anything is wrong.
The Latest on: Voice analysis
via Google News
The Latest on: Voice analysis
- AI-based voice analysis app monitors mentally ill patientson January 22, 2020 at 9:20 pm
An interactive voice application that leverages artificial intelligence is able to track the ... According to results of the study, published last week in the journal PLOS One, the app’s analysis was ...
- Chatbot Market Size 2019 Share, Trends, Industry Analysis, Segmentation and Forecast by 2025 | CAGR of 26.9%on January 22, 2020 at 8:11 pm
Global Chatbot market is valued at USD 435.2 Million in 2018 and expected to reach USD 2306.3 Million by 2025 with a CAGR of 26.9% over the forecast period. Growing popularity of online messaging ...
- Voice Biometrics Market Trends, Growth, Scope, Size, Overall Analysis and Prognostication by 2025on January 21, 2020 at 9:59 am
An analysis of Voice Biometrics Market has been provided in the latest report launched by DataIntelo.com that primarily focuses on the market trends, demand spectrum, and future prospects of this ...
- Instreamatic Partners with Triton Digital to Accelerate Voice-Driven Audio Advertisingon January 21, 2020 at 6:00 am
Instreamatic utilizes advanced voice AI and deep learning mechanisms that analyze and iteratively improve understanding of listener responses and the delivery of dialogue ad content. Advertisers are ...
- Latest Wrap on Voice to Text on Mobile Devices: Market Development Scenario that’s changingon January 21, 2020 at 3:59 am
... Voice to Text on Mobile Devices Market Research Report Sample The Global Voice to Text on Mobile Devices market report gives an exceptional and presentable analysis of the market size, patterns, ...
- Voice-analysis application assesses well-being of patients with serious mental illnesson January 20, 2020 at 10:39 am
These responses were then analyzed by an application called MyCoachConnect — an artificial intelligence program that used an individual's words to offer a personalized analysis for each patient. It ...
- Voice Analysis Software Market: Competitive Dynamics & Global Outlook 2026on January 18, 2020 at 6:15 pm
Market Research Report Store offers a latest published report on Voice Analysis Software Market Analysis and Forecast 2019-2025 delivering key insights and providing a competitive advantage to clients ...
- How a heritage of black preaching shaped MLK's voice in calling for justiceon January 17, 2020 at 4:01 pm
Gilbert, Howard University (THE CONVERSATION) The name Martin Luther King Jr. is iconic in the United States. President Barack Obama mentioned King in both his Democratic National Convention ...
- App uses voice analysis, AI to track wellness of people with mental illnesson January 17, 2020 at 12:38 pm
participants called a toll-free number one or two times a week and answered three open-ended questions when prompted by a computer-generated voice. The questions were: How have you been over the past ...
- Cockpit Voice and Flight Data Recorder Market Executive Summary, Introduction, Sizing, Analysis and Forecast To 2029on January 17, 2020 at 1:46 am
Global News for Cockpit Voice and Flight Data Recorder Market Study 2020-2029, by Segment (Flight Data Recorder (FDR), Cockpit Voice Recorder (CVR), Combined Voice and Flight Data Recorder (CVFDR)), ...
via Bing News