Voice-based querying for information is a powerful technology that can hugely enhance the scope of information retrieval systems by enabling their remote access via the ubiquitous mobile phone. Information retrieval based on automatic speech recognition however is challenging due to the environment noise and speaker idiosyncrasies typical of real-world scenarios. Wherever possible, strong domain constraints on the language model are used to minimize the impact of signal degradation on recognizer performance. This can lead to grossly mismatched utterance and hypothesis occasionally, a situation that must be detected to protect the call from irrecoverable errors. We consider the revalidation of recognition hypotheses via confidence measures derived from forced alignment using an independent decoder. We show that our FST based aligner can accurately reject incorrect decoder hypotheses while being particularly robust to the phenomenon of incomplete utterances.