The first link you cited is spot on. The authors are from Univ of Cambridge, and work on HTK <http://htk.eng.cam.ac.uk/>.
That paper is 10 years old. As I'm sure you can imagine, there have been improvements in the field since then. To be completely honest, I don't stay on top of search applied to speech, but the keyword you want is "Spoken Document Retrieval" (SDR). Ciprian Chelba and TJ Hazen do cool stuff in this area; they are giving a tutorial at ICASSP this year SDR.
An aside. Both of these approaches use the fact that when you process speech, you essentially form a graph of words (or phonemes). Paths through the graph represent possible transcriptions. So, since graph is a denser, richer thing to search than the transcript, and we've got graph algorithms sitting around, there are neat tricks you can do to build a search engine index for speech...
I've recently been reading some interesting work that uses locality-sensitive hashing to search audio. The Google speech people are presenting a lot of it at ICASSP this year. See this post for more, and chase the links in their papers for even more: <http://googleresearch.blogspot.com/2007/02/hear-here-sample-of-audio-processing.html>