Overview
The goal of the Speech Accessibility Project at the University of Illinois Beckman Institute
(https://speechaccessibilityproject.beckman.illinois.edu) is to collect, annotate, and curate
a shared database of speech samples from people with disordered speech, and share this data set
with researchers at other organizations. This project plans to collect 1,200,000
speech samples from 2,000 people, each of whom will provide 600 samples.
The initial focus will be people with Parkinson's disease (PD), Multisystem Atrophy (MSA), Progressive Supranuclear Palsy (PSP), Lewy Body Disease (LBD), Cortical Basal degeneration (CBD) and Post Deep Brain Stimulation (Post-DBS).
UIUC will build an open-source software infrastructure to collect annotated
speech samples and share these data in an appropriately secure fashion with researchers from
our partner technology companies (and eventually, other organizations as well) so that they
can use these data to improve their automatic speech recognition algorithms. This project
promotes diversity, equity, and inclusion by helping technology companies to fully support
all types of speech, and it is also more efficient and less burdensome for these specialized
patient populations to have one centralized "collector" of speech samples.
Description
The goal of the entire project is to collect 1,200,000 speech samples from 2,000 people with
dysarthria, where we expect to collect data from 400 people each from five different patient
populations. Each person would provide 600 speech samples.
(600 samples/person x 400 persons/etiology x 5 etiologies = 1,200,000 samples)
Our schedule of research procedures is:
- March 2023-December 2024: data collection of speech samples from 400 people with PD and related disorders.
Data collection of speech samples from the PD cohort will be a collaboration of the University of
Illinois and of speech clinician mentors from LSVT Global (Lee Silverman Voice Treatment); data collection is virtual.
Potential
participants will be screened both with a questionnaire and by providing a short set of
"quality control" speech samples. If the participant does not pass screening, they will be
thanked for their interest. Otherwise, the participant is eligible for the study and can do
the informed consent process and then engage in contributing speech samples.
Participants can do as many recordings as they wish on their home computer or smart phone at whatever time of day is convenient for
them. Participants will be able to login to the system at any time, 24/7.
Participants have the opportunity to earn $180.00 and their helper can earn $90.00.
Participants also have the option to provide additional data about themselves, such as their
age, race and ethnicity, and the year of their diagnosis. These "metadata tags" are
completely optional but are helpful for analysis.
The collected speech samples will be stored securely in a custom database built by the UIUC
Beckman Institute. All samples are stored with a unique participant ID code. All samples are
annotated by our UIUC research team with technical information about the acoustic waveform
and other information.
The entire database of speech samples will be shared with our coalition partners (Amazon,
Apple, Google, Meta, and Microsoft), and, after all data collection is complete, with other
universities and companies who are willing to sign our data use agreement. Each partner has
signed a data use agreement with UIUC that allows these deidentified data to be used for
improvements in speech recognition technology and assures the privacy of participants and
confidentiality of data.
Eligibility
Inclusion Criteria:
- Adult (age >= 18 years)
- Self-reported diagnosis of: PD, MSA, PSP, LBD, CBD, Post-DBS
- Reads and speaks English
- Has a valid email address
- Ability to access web browser to participate in the study
- Is a resident of Canada or the United States (excluding the states of Illinois, Texas, and Washington)
Exclusion Criteria:
- Is a resident of the State of Washington, Texas, or Illinois (because these states have privacy laws that would not allow us to collect 'voice prints')
- If quality control screening of initial speech samples "fails" because of poor data quality (e.g., noisy recording environment)