I find Alex’s comments pretty openly inflammatory and sensationalist. It’s a pretty far throw from a system that predicts a face from a voice to transphobia.
On the other hand, I think it's legitimate to criticize research that has many obvious bad applications and few obvious good applications.
(Perhaps an important factor is why this research was being done in the first place? Did they build a "reconstruct what a person probably looks like from their voice" just to see if they could, or did they have some real application in mind? For instance, suppose they were working with a group of oceanographers who wanted a system to infer the characteristics of orcas from their whalesong as part of a project to track their migration habits, and the researchers decided to test their system on people first because it's easier and less expensive. If a project was undertaken for a good reason, but it opens the door to a lot of bad applications as an unintentional side-effect, should the researchers get a free pass on the latter?)
Just like the tool can not know if I have long or short hair based on my voice, it also won't be able to know anything else that has deviated from the average.
This here is the problem, and public discourse has a solution to the word you're seeking: transinclusive.
"-inclusive" succinctly provides a hint on how to be considerate, while being optimistic.
Hate & phobia issues aside.
Edit: Most research begins describing exactly why the research was undertaken.
We're all abnormal, which is why it's important to be careful how we use statistical models, but the acknowledgment and exploitation of normality is not an attack against people who deviate from it, it is the foundation of statistics and all of science. Calling the authors bigots does nothing to improve these issues, rather the opposite.