# Computational Speech Analysis in Alzheimer's Disease and Other Neurocognitive Disorders

> **NIH NIH K23** · UNIVERSITY OF COLORADO DENVER · 2021 · $188,298

## Abstract

Abstract:
Early and accurate diagnosis of neurocognitive disorders (NCDs) is critical for planning, treatment, and
research referral, but demands time and expertise often unavailable to primary care providers. Speech and
language are often impaired early in the disease course of several NCDs. Previous research has demonstrated
the diagnostic potential of computer speech analysis (CSA), with differences between healthy controls and
disorders such as mild cognitive impairment (MCI) and Alzheimer's disease. However, there are several
additional steps that must be taken to make CSA a diagnostically viable screening tool. This proposal includes
a career development plan providing the applicant with training, mentorship, and experience in the following
areas in order to bring CSA techniques into clinical practice: 1) computational linguistics and paralinguistics, 2)
longitudinal markers of disease, and 3) design of novel technology for dissemination. As part of this training,
academic and professional skills, including ethics in research, will also be expanded. Uniquely qualified
mentorship and advisory teams have been selected to ensure the success of the proposed training and
research.
The proposed study is a prospective, longitudinal, observational, cohort investigation of two distinct research
groups. The first group is a highly selected and well-characterized research cohort of healthy control,
Alzheimer's disease, and MCI subjects (Group A). In Group A, the performance and reproducibility of a
machine learning algorithm will be improved to distinguish Alzheimer's disease and MCI from healthy controls
using CSA. Multiple regression and voxel-based morphometry will be used to better understand what may
drive group differences in CSA measures in Group A as well. Clinical applications of this algorithm will then be
assessed in a clinic-based cohort of patients with different NCDs (Group B) in order reduce spectrum bias
likely present in prior studies. As sub-aims in both groups, possible further improvement of the algorithmic
outcomes with longitudinal CSA measures will also be examined. The overall objective is to develop intuitive,
reliable and reproducible CSA-based clinical measures by correlating them with established neuropsychiatric
and imaging markers, determining their efficacy in clinical populations, and determining how they change over
time. As a result, this research will validate specific speech traits as useful diagnostic markers of
neurocognitive disease and explain why those markers differ between patient groups, both of which are major
steps towards the design of novel and easily implemented tools in the screening of NCDs such as Alzheimer's
disease.

## Key facts

- **NIH application ID:** 10145566
- **Project number:** 5K23AG063900-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Peter Scott Pressman
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $188,298
- **Award type:** 5
- **Project period:** 2020-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145566

## Citation

> US National Institutes of Health, RePORTER application 10145566, Computational Speech Analysis in Alzheimer's Disease and Other Neurocognitive Disorders (5K23AG063900-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10145566. Licensed CC0.

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