Given the size of typical password spaces, knowledge of the distribution of user passwords is essential to an adversary. Without such knowledge the adversary has no way of directing her search toward more probable passwords, and is no better off than if users really did pick their passwords uniformly from the set of possibilities .
Where did the knowledge of the distribution come from in the case of textual passwords? For the most part, dictionaries have been compiled by using reasonable assumptions about likely choices. The assumptions stem from the use of a shared language, and shared knowledge of the semantic content of words. For example, in the work of Klein  the sources for likely passwords included the St. James Bible, the Unix dictionary, and many other sources of English words that were available to the author precisely because they are a part of our language. If these assumptions had turned out to be incorrect, textual password schemes would be extremely difficult to break in practice.
The assumptions made about likely password choices are strongly confirmed by Klein's work, and by successful attacks on textual passwords, but confirmation of pre-existing dictionaries is not the same as deriving a dictionary in the first place by learning from example without prior knowledge. In the case of textual passwords, this would mean learning the English dictionary (or some equivalent corpus of words) by collecting user passwords. This would involve acquiring millions of verified passwords, and, as such, represents a significant challenge for a would-be adversary.
In the case of the DAS scheme, similar reasonable assumptions about user choice do not exist. Furthermore, the learning task is made even more difficult by two factors. First, our previous arguments suggest that both the space of passwords and the space of likely user choices are considerably larger than for textual passwords. Second, the platform that we are targeting, PDAs, renders the task of data collection much harder than on, e.g., networked computers.