# Generating Personalized Synthetic Speech for Progressive Dysarthria Using Severity-Appropriate Adaptation Strategies for Neural Text-to-Speech and Voice Conversion

> **NIH NIH R21** · UNIVERSITY OF MISSOURI-COLUMBIA · 2022 · $226,312

## Abstract

PROJECT SUMMARY
More than 2 million Americans have a complex communication disorder that impairs their ability to talk. The loss
of speech is among the most debilitating effects of neurological diseases like amyotrophic lateral sclerosis (ALS),
where 95% will progressively lose their ability to speak and get trapped in a state of isolation. Communication
devices with electronic voice output allow patients to augment or replace verbal communication as their speech
deteriorates. The text (alphabet, messages) available on these devices is accessed directly using functioning
body parts (fingers, head, eyes), and the selected text is converted to speech through text-to-speech (TTS)
technology. Electronic TTS voices available on current devices have limited options in terms of age, sex, and/or
dialect, which diminishes the experience of a genuine discourse because neither the user nor their
communication partner can relate to the device voice. Voice is an integral part of a person’s identity and without
a voice that captures this identity, users tend to withdraw from interactions, greatly reducing their quality of life,
and leading to low acceptance of the technology. Personalized TTS voice options are a critical need for the ALS
population in order for them to be able to communicate freely in the face of major life changes.
The long-term goal of this research is software-based, high-performance personalized speech synthesis that can
be used on mobile platforms and commercial speech devices by people with communication disorders. Our
short-term goal is to investigate innovative methods that leverage state-of-the-art, end-to-end neural TTS, to
generate intelligible, natural, and personalized synthetic speech for people who already exhibit speech loss from
ALS. Neural TTS has significantly outperformed the previous generations of TTS technology, and has lowered
the barrier to develop high-quality TTS systems. While it is clearly desirable to use neural TTS, the need for large
quantities of high-quality speech data prohibits training such a system directly for those with ALS. We address
this problem through our two specific aims in this exploratory project: (i) adapt neural TTS output by using voice
conversion to personalize TTS voice options for ALS and (ii) adapt neural TTS input features and network
parameters to personalize TTS voice options for ALS. Our methods for both aims will preserve TTS speech
intelligibility and naturalness while enhancing voice similarity, by using modest amounts of speech data from
persons with ALS.
Our adapted neural TTS system is expected to generate personalized synthetic speech that has the voice
characteristics of individual ALS users along with intelligibility and naturalness to promote communication and
listening comfort. The project goals align with NIH-NIDCD’s priority area related to “Advancing Research in Novel
Augmentative and Alternative Communication (AAC) Approaches”. The project outcomes are expected to
provi...

## Key facts

- **NIH application ID:** 10525903
- **Project number:** 1R21DC019952-01A1
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Mili Kuruvilla-Dugdale
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $226,312
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10525903

## Citation

> US National Institutes of Health, RePORTER application 10525903, Generating Personalized Synthetic Speech for Progressive Dysarthria Using Severity-Appropriate Adaptation Strategies for Neural Text-to-Speech and Voice Conversion (1R21DC019952-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10525903. Licensed CC0.

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