University of Illinois Urbana-Champaign, USA has announced the Speech Accessibility Project, a new research initiative to make voice recognition technology more useful for people with a range of diverse speech patterns & disabilities.
The project launches with cross-industry support from Amazon, Apple, Google, Meta, and Microsoft, as well as nonprofit organizations whose communities will benefit from this accessibility initiative, to make speech recognition more inclusive of diverse speech patterns, according to a university press release.
“The option to communicate and operate devices with speech is crucial for anyone interacting with technology or the digital economy today,” said Mark Hasegawa-Johnson, the UIUC professor of electrical and computer engineering leading the project in the press release. “Speech interfaces should be available to everybody, and that includes people with disabilities. This task has been difficult because it requires a lot of infrastructure, ideally the kind that can be supported by leading technology companies, so we’ve created a uniquely interdisciplinary team with expertise in linguistics, speech, AI, security, and privacy to help us meet this important challenge.”
Today’s speech recognition systems, such as voice assistants & translation tools, don’t always recognize people with a diversity of speech patterns often associated with disabilities. This includes speech affected by Lou Gehrig’s disease or Amyotrophic Lateral Sclerosis, Parkinson’s disease, cerebral palsy, & Down syndrome. In effect, many individuals in these & other communities may be unable to benefit from the latest speech recognition tools.
With artificial intelligence & machine learning, technology companies can address the need for more inclusive speech recognition. To support this goal, The Speech Accessibility Project will collect speech samples from individuals representing a diversity of speech patterns. UIUC researchers will recruit paid volunteers to contribute recorded voice samples & will create a private, de-identified dataset which can be used to train machine learning models to better understand a wide variety of speech patterns. The Speech Accessibility Project will focus 1st on American English.
