# Detecting and classifying non-fluent speech in aphasia using machine learning

> **NIH NIH F32** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $74,302

## Abstract

PROJECT SUMMARY
Among the approximately 2 million Americans living with post-stroke aphasia, many experience difficulties with
verbal expression that render everyday communication effortful, inefficient, and stressful.1,32 For persons with
aphasia (PWA), speech non-fluency is often experienced as a visible disability with significant social
consequences.36,37 Given this functional salience, speech fluency is an important construct to assess, monitor,
and treat. It is, however, a longstanding clinical challenge to index fluency in a way that is comprehensive,
interpretable, and efficient,7 and current approaches rely on either expert clinician ratings or time-intensive
linguistic analyses using detailed coding. Temporal acoustic measures, by contrast, are objective measures
that can be automatically or semi-automatically derived from connected speech. Prior research has
demonstrated that the rate and rhythm of speech output reflect underlying impairments in both speech and
language (e.g., motor speech, lexical retrieval), suggesting the utility of temporal acoustic measures to index
non-fluency in PWA. The goal of the current study is to investigate the feasibility of using automated temporal
acoustic features to identify non-fluent aphasia and to better understand the latent speech, language, and
cognitive constructs underlying these surface speech features. To achieve this goal, we leverage machine
learning techniques as part of a predictive modeling approach to identify speech features whose clinical utility
can be generalized to inform future assessment of fluency in aphasia. In Aim 1, we will investigate whether
temporal acoustic features accurately predict fluency status using a supervised machine learning approach
(Aim 1a), and which features are most important to clinical distinctions of interest (fluent v. non-fluent; present
v. absent motor speech impairment; Aim 1b). In Aim 2, we will determine the underlying speech, language, and
cognitive contributors to inter-individual variability in temporal acoustic measures, thereby augmenting the
explanatory power of study results. These aims are a first step toward an interpretable and automatable
predictive model of fluency in PWA that can be generalized to novel diagnostic situations. Results of this
research will help clinicians identify important features for efficient assessment of and treatment planning for
patients as well as provide a mechanistic understanding of surface level features by mapping those features to
explanatory clinical sub-constructs.

## Key facts

- **NIH application ID:** 10459913
- **Project number:** 1F32DC020342-01
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Claire Elizabeth Cordella
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $74,302
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10459913

## Citation

> US National Institutes of Health, RePORTER application 10459913, Detecting and classifying non-fluent speech in aphasia using machine learning (1F32DC020342-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10459913. Licensed CC0.

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