Early Detection of Cognitive Decline

Detecting the earliest stage of neurodegenerative disorders by automatic analysis of speech using deep learning

Degree type

PhD

Closing date

10 October 2022

Campus

Hobart

Citizenship requirement

Domestic/International

About the research project

Neurodegenerative conditions like dementia affect millions and have no known cure, making early detection important. In Australia, more than 459,000 people are living with dementia, at a cost of $15 billion per year. With ageing populations, the prevalence is rapidly rising and is expected to be over 1 million by 2058 (Health Direct Australia, 2019). The primary aim of this project is to investigate AI based early detection algorithms using the speech and text data collected from people with neurodegenerative conditions.

There is evidence that spoken language is an informed representation of an individual's cognitive status (Asgari et al., 2017) and that speech analysis could potentially be used as a quick test of 'pre-symptomatic' cognitive status (Kindell et al., 2017). Recently, new research evidence shows that spoken language reveals cognitive status, and speech analysis can classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy control (HC) group (Luz et al., 2020). However, the current analysis of language impairment depends on intensive labor, since high-fidelity speech transcripts are needed (Asgari et al., 2017). Hence this multi-discipline project aims to fill the gap with an automated language-based assessment that offers a broad availability for potential patients with neurodegenerative disorders and facilitates further corresponding treatment.

We will train an algorithm to differentiate those adults with normal cognition from those with accelerated cognitive decline. Machine learning and knowledge discovery methods will map how combinations of speech features discriminate normal ageing from accelerated cognitive ageing. Mixed methods of audio and text analysis are emerging as potential early indicators of cognitive decline. Therefore, there are two processes will be conducted: audio analysis, and transcript analysis.

Primary Supervisor

Meet Dr Mira Park

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Additional eligibility criteria specific to this project/scholarship:

  • Applicants must be able to undertake the project on-campus

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Application process

There is a three-step application process:

  1. Select your project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Mira Park to discuss your suitability and the project's requirements; and
  3. Submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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