Me: Excuse me, do you have any <snicker> Dogwood trees?
Doggo: Sir, that's a bit insensitive, and borderline offensive.
Me: Sorry... so, what do you recommend?
Doggo: Follow me, I'll show you all my birches <snicker>
Me: LOL
Doggo: LOL
Me: Hahhh... how are you even talking?
Doggo: Duolingo

If you really want to rigorously test this, take a large, diverse sample of people, put each one in an fMRI and watch the brain activity in real-time as they answer questions regarding preference for various synthesized voices on different kinds of technology. Personal assistants, navigation, you must construct additional pylons, give them a variety of different examples, watch which parts of the brain light up when listening to each one, and match it up to which ones they like best and which ones they like least. Then make a correlation map for which brain areas are most active for any given choice.

Then, get a second sample set of people, do the same thing, but sort through the results looking only at the brain activity and make a prediction of what their answers were based on how that activity matches up with the previously established map. Eg. We note that activity in [this] part of the brain indicates preference for female voice in receiving directions. This person has activity in that same part of the brain, so we predict that their answer was a preference for female voice. The more accurate the predictions, the stronger the model. This is where AI analysis and deep learning can truly shine.