# Automated objective outcome measures for clinical use in dysarthria

> **NIH NIH R44** · AURAL ANALYTICS, INC. · 2022 · $734,433

## Abstract

Abstract / Summary
The inability to engage in spoken communication is among the most debilitating of all human conditions. In the
field of communication disorders, a speech-language pathologist’s (SLP’s) perceptual evaluation of the quality
of speech production is the gold standard for assessment and for documenting treatment progress. However,
decades of research have confirmed that auditory-perceptual judgments of speech are inherently biased,
which compromises reliability. The reason is that the human perceptual system is adaptive, with perceptual
bias accrued by working with an individual across multiple treatment sessions, or by working with patient
populations across a career. Thus, to reliably document treatment outcomes subjectively, it is necessary to
involve multiple, unfamiliar listeners. This is untenable in most clinical settings, which means that subjective
impressions are made by the treating clinicians. The reliance on subjective evaluation directly undermines the
quality of clinical practice and a clinician’s ability to demonstrate the efficacy of an intervention.
Aural Analytics has developed new objective acoustic speech metrics that reliably measure speech in
populations with neurological disorders. Its technology is based on a strong scientific premise and has been
adopted early by pharmaceutical companies and neurologists in clinical research. Aural Analytics has collected
and analyzed tens of thousands of speech samples using its technology, and the results are demonstrating
that its measures are robust, reliable, and more sensitive to longitudinal changes in speech than are other
existing outcome measures. We successfully completed a Phase I SBIR project with the aim of translating our
technology to SLP clinical practice. This Phase II proposal naturally builds on our previous work by connecting
the automated app-based outcome measures completed in Phase I to three complementary clinical
benchmarks. Specifically, SA1 will validate the Aural Analytics speech measures against the American
Speech-Language-Hearing Association’s (ASHA) Functional Communication Measures (FCM) for motor
speech; the Sentence Intelligibility Test; and expert ratings of speech characteristics. In addition, age and
gender-based norms for all objective measures will be obtained from collection of data from 600 new healthy
participants. In SA2, Aural Analytics will conduct a usability study with practicing speech-language pathologists
to assess real world utility and refine the user experience. The deliverable of this proposal will be a
fully-functional mobile application, validated by practicing SLPs in a clinical setting, with real time speech
outcome metrics validated with respect to existing community-accepted measures. This will result in objective
outcomes that fit into the workflow of the professional standard, thereby expediting our path to
commercialization.

## Key facts

- **NIH application ID:** 10323563
- **Project number:** 2R44DC017625-02A1
- **Recipient organization:** AURAL ANALYTICS, INC.
- **Principal Investigator:** Sherman Charles
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $734,433
- **Award type:** 2
- **Project period:** 2019-01-07 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10323563

## Citation

> US National Institutes of Health, RePORTER application 10323563, Automated objective outcome measures for clinical use in dysarthria (2R44DC017625-02A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10323563. Licensed CC0.

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