#voice-accessibility

#voice-accessibility

Cohere's Transcribe model is designed for tasks like note-taking and speech analysis, supporting 14 languages and optimized for consumer-grade GPUs, making it accessible for self-hosting.

In 2003, when plumbing fixtures industry veteran Rob Buete first encountered the "walk-in tub" made by a startup called Safety Tub, he burst out laughing. A bathtub with a door? It seemed like a joke, or at best a clunky contraption for frail seniors who couldn't step over a regular tub. Kinya Seto is the CEO of LIXIL, the global manufacturer of pioneering water and housing products, including brands such as GROHE, American Standard, INAX, and Tostem.

When I moved in here it truly was my last resort. Since living here I feel like I have the same independent life that my friends have and I just don't want to lose that. The guide dog run is probably the most important thing for me. It's a safe and confined area where I feel comfortable taking my dog out, especially at night.

For decades, people with disabilities have relied on service dogs to help them perform daily tasks like opening doors, turning on lights, or alerting caregivers to emergencies. By some estimates, there are 500,000 service dogs in the U.S., but little attention has been paid to the fact that these dogs have been trained to interact with interfaces that are made for humans.

Ana proposed the following: Is this enough in 2026? As an occasional purveyor of the visually-hidden class myself, the question wriggled its way into my brain. I felt compelled to investigate the whole ordeal. Spoiler: I do not have a satisfactory yes-or-no answer, but I do have a wall of text!

My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow. There were no defined user archetypes guiding the query creation process. Team members were essentially reverse-engineering the work: you think of a task, write a query to help the agent execute it, and cross your fingers that it aligns with the needs of a hypothetical "ideal" user - one who might not even exist.