In the DIPLOMAT system, the Sphinx-II continuous speech recognizer is used to transcribe the user's spoken utterance into text. This text, after an opportunity to correct recognition errors, is translated and then synthesized in the other language using the Phonebox concatenative speech synthesizer developed at CMU.
DIPLOMAT is a bi-directional system for translating a conversation, so it uses two copies of the translator software. This not only permits the translation of each of the conversants' speech, but also gives us the opportunity of providing a back-translation. If the system's back-translation of its output correctly conveys the meaning of the original input, we have much greater confidence that it actually translated things correctly.
A common method of translating the query is to look up each word in a bilingual dictionary, and replacing it with every possible translation listed for that word. This produces a new query in the other language, which can then be used with standard monolingual retrieval systems. Statistically-generated dictionaries can be used with this method simply by replacing the general-purpose dictionary with the statistical one, but it is possible to do even better. In addition to being attuned to the actual usage of words in the training corpus (and thus sometimes listing translations which would not appear in a general-purpose dictionary), a statistically-generated dictionary also contains frequency information -- which we can use to give greater importance to the more common translations.
Now, one drawback of a statistically-generated dictionary is that it contains a lot of erroneous "translations", but it turns out that these "errors" are actually beneficial for information retrieval. Since the process of generating the dictionary picks words in the other language that are highly correlated with the source-language word, the incorrect translations will be terms that provide a useful expansion of the query (i.e. the dictionary may translate "Hillary" as both "Hillary" and "Clinton").
For full details on applying statistical dictionaries to Cross-Language Retrieval, see the paper " Automatically-Extracted Thesauri for Cross-Language IR".