Lloyd May - Research

Research

Below are some highlighted recent research projects. Please see my Google Scholar for a complete, up-to-date list of publications

Closed-Caption Personalization

How can we adjust closed captions so that they meet the actual needs of users?

My previous work in this space has looked at non-speech sound captioning practices on YouTube, an overview of NSI captioning issues & opportunities, software to create customizable Rich Captions, and the Selection, Curation, Communication framework for NSI captioning.

Music Personalization for Cochlear Implant Users

What tools would empower cochlear implant (CI) users to engage more meaningfully with recorded music?

We explored this question by pairing professional audio engineers and CI users to create bespoke mixes, and then designed a website centered on these findings to give CI users agency to control parameters themselves.

Streamlining Video Game Accessibility Settings

What features would a holistic system have to streamline the communication, implementation, and navigation of accessibility-related settings in video games?

We interviewed both developers and players with Disabilities to design Access Profiles, a system that tries to unite the disparate pieces of accessibility infrastructure.

Open Projects

Supporting Indie Game Developers in Making Games Accessible

How can Indie developers be supported in implementing accessibility-related features into their games? This might involve a mix of interviewing Indie devs & Disabled gamers, as well as the design and development of plug-ins, wikis, or other software tools.

Rich Captions in the Wild

What factors impact users' preferences for closed-captioning? This project would involve a longitudinal evaluation of customizable captions with people who use captions for access (e.g., Deaf and Hard-of-Hearing folks, neurodiverse folks, etc.)

Music Personalization using Real-Time Source Separation

What features do CI users most desire in a real-time music personalization system? The project would involve the development of an app to perform music source separation in (close to) real-time, and give users various options to personalize the mix.

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