Information retrieval methods are frequently used for indexing and retrieving spoken documents, and more recently have been proposed for voice-search amongst a predefined set of business entries. In this paper, we show that these methods can be used in an even more fundamental way, as the core component in a continuous speech recognizer. Speech is initially processed and represented as a sequence of discrete symbols, specifically phoneme or multi-phone units. Recognition then operates on this sequence. The recognizer is segment-based, and the acoustic score for labeling a segment with a word is based on the TF-IDF similarity between the subword units detected in the segment, and those typically seen in association with the word. We present promising results on both a voice search task and the Wall Street Journal task. The development of this method brings us one step closer to being able to do speech recognition based on the detection of sub-word audio attributes.