I haven't tried it, as you get asked for the answers to common security questions, and supply a voice sample. You could lie but many people won't.
My other issue is that it will have been trained on a large number of voice samples and no one will learn how to distinguish different accents by using it, or even by developing it.
An alternative, knowledge-based approach, would work by splitting the speech into phonemes and matching phonemes/sequences of phonemes against known accents or foreign languages, e.g. if a native speaker rhymes "good" and "food", you can reliably tell they're either from Scotland or Ulster. Telling close accents apart is easy with the right phrases, e.g. "fish and chips" (Australian vs. New Zealand), "I saw the White House" (General American vs. Canadian). For non-native speakers, you can use the phoneme inventory of their native language, so if someone has difficulty in pronouncing "th" you can rule out Greek or Spanish (from Spain), and if someone has difficulty in pronouncing "f" they're probably Korean.
Of course, that's a lot of work up front, but you'd learn a lot in the process of developing such a system, and it would be able to explain its decisions to users. And by asking you to repeat standard phrases (like "good food") you would allay security concerns.