9 ranking. However, once enough data has been acquired the difference in perplexity between the two ranking systems is small. We reached two conclusions from this experiment: either of the two ranking systems is better than not doing any ranking and the predictive ranking has a definite advantage at covering the in-domain vocabulary. The amount of data used for these language models is small compared to the previous experiments from Table III. As a result, the perplexities are much larger. However, if more data is downloaded the size of the queue will expand and the predictive ranking will continue to have an advantage. In a real-world application, the predictive ranking model would be retrained as additional batches of users are downloaded for maximum performance. We have shown that Twitter data is useful for modeling conversational speech in a variety of languages. The high weights assigned to the Twitter data in the mixture models proves the point. As a point of comparison, using Turkish newspaper text in the mixture model only got 2% weight compared to the 27% weight assigned to the Twitter data. The combination of Twitter data with the use of class language models is an especially strong result. The approximately 20% reduction in perplexity would be hard to achieve by means of collecting additional in-domain speech data without incurring significant collection costs. There are still many pieces of unexploited information that could be used to improve the language modeling. For example, further analysis and clustering of the Twitter social graph could reveal linguistic subgroups. Additionally, selected Tweets have GPS tagging enabled, which could be used to automatically identify and model regional dialects. In the introduction we mention that one reason to prefer collecting data from Twitter is the ease and speed with which the data can be collected. Our method for prioritizing the download queue builds off this advantage to increase the amount of data collected and maximize the vocabulary coverage, which justifies increased effort in searching and crawling. Since the data gathered using the predictive ranking contains many vocabulary types from held-out data, it could be used for vocabulary expansion. The real-world benefit of our predictive ranking approach is sensitive to the rate limits of the Twitter API. For simplicity, these considerations were not taken into account in our experiments but, should be addressed in future work. REFERENCES VI. CONCLUSIONS AND FUTURE WORK Fig. 3. Vocabulary Type Coverage by Crawling Strategy TABLE VI QUEUE RANKING STRATEGY LM PERPLEXITY COMPARISON 150 Users 300 Users 500 Users Predictive Ranking 533 512 490 Edge Ranking 547 524 491 Random Baseline 581 572 559 [1] A. Marin, S. Feldman, M. Ostendorf, and M. Gupta, “Filtering web text to match target genres,” in Proc. ICASSP , 2009, pp. 3705–3708. [2] X. Zhu and R. Rosenfeld, “Improving trigram language modeling with the World Wide Web,” in Proc. ICASSP , 2001, pp. I:533–536. [3] R. Rosenfeld, “Two decades of statistical language modeling: where do we go from here?” Proc. IEEE , vol. 88, no. 8, pp. 1270–1278, 2000. [4] I. Bulyko, M. Ostendorf, and A. Stolcke, “Getting more mileage from