Mobile is poised to become the predominant platform over which people are accessing the World Wide Web. Recent developments in speech recognition and understanding, backed by high bandwidth coverage and high quality speech signal acquisition on smartphones and tablets are presenting the users with the choice of speaking their web search queries instead of typing them. A critical component of a speech recognition system targeting web search is the language model. The chapter presents an empirical exploration of the google.com query stream with the end goal of high quality statistical language modeling for mobile voice search. Our experiments show that after text normalization the query stream is not as ``wild'' as it seems at first sight. One can achieve out-of-vocabulary rates below 1%% using a one million word vocabulary, and excellent n-gram hit ratios of 77/88%% even at high orders such as n=5/4, respectively. A more careful analysis shows that a significantly larger vocabulary (approx. 10 million words) may be required to guarantee at most 1%% out-of-vocabulary rate for a large percentage (95%%) of users. Using large scale, distributed language models can improve performance significantly---up to 10%% relative reductions in word-error-rate over conventional models used in speech recognition. We also find that the query stream is non-stationary, which means that adding more past training data beyond a certain point provides diminishing returns, and may even degrade performance slightly. Perhaps less surprisingly, we have shown that locale matters significantly for English query data across USA, Great Britain and Australia. In an attempt to leverage the speech data in voice search logs, we successfully build large-scale discriminative N-gram language models and derive small but significant gains in recognition performance.