This paper presents a strategy for efficiently selecting informative data from large corpora of untranscribed speech. Confidence-based selection methods (i.e., selecting utterances we are least confident about) have been a popular approach, though they only look at the top hypothesis when selecting utterances and tend to select outliers, therefore, not always improving overall recognition accuracy. Alternatively, we propose a method for selecting data looking at competing hypothesis by computing entropy of N-best hypothesis decoded by the baseline acoustic model. In addition we address the issue of outliers by calculating how representative a specific utterance is to all other unselected utterances via a tf-idf score. Experiments show that N-best entropy based selection (%relative 5.8 in 400-hour corpus) outperformed other conventional selection strategies; confidence based and lattice entropy based, and that tf-idf based representativeness improved the model further (%relative 6.2). A comparison with random selection is also presented. Finally model size impact is discussed. © 2012 IEEE.