We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages. We scrape text from multiple genres including blogs, online news, translated TED talks, and subtitles. Using linearly interpolated language models, we find that blogs and movie subtitles are more relevant for language modeling of conversational telephone speech and obtain large reductions in out-of-vocabulary keywords. Furthermore, we show that the web data can improve Term Error Rate Performance by 3.8% absolute and Maximum Term-Weighted Value in Keyword Search by 0.0076-0.1059 absolute points. Much of the gain comes from the reduction of out-of-vocabulary items.