Their language models might perform acceptably with text, but speech is much looser with structure and grammar. They may apply a bit of local expectation-maximisation but anything more strict or long-range wouldn't work.
It may be computationally impractical though.
So good transcriptions would be good, regardless of the noise environment. It might give some false positives, but I expect that a good price to pay to avoid the kind of mess they create now.
For example, the difference between "its" and "it's", "red" and "read", "know" and "no" is irreconciliable without understanding the rest of the sentence these trouble words appear in.
What I'm proposing is to pass the output of speech recognition through a binary classifier that answers the question "is this text gibberish?", which is trained with the help of a textual language model, unrelated to the speech recognition pass.