The speech recognition example from a Nexus One phone elsewhere in the comment thread was pretty good on the level of individual words. Pattern matching learned

The speech recognition example from a Nexus One phone elsewhere in the comment thread was pretty good on the level of individual words. Pattern matching learned with a massive teaching corpus seems to go pretty far. The example didn't make sense on the level of sentences though. Problems start with homophonic words. Speech is pretty noisy, and there are words whose sounds you can't distinguish even in theory, but which still need to be picked correctly when transcribing speech. My guess is that humans are good at this by reconstructing a word string that roughly matches the sounds heard and seems to be saying something sensible. (You could test this easily by having one person read out computer generated word soup nonsense and have another person listen and transcribe what they hear.) The big problem with speech recognition would then be that recognizing meaningful messages from the various likely interpretations of the speech sounds is pretty much AI complete for the general case. You could try to do the Google translate approach and use a huge amount of existing text to teach the system about the type of sentences people write a lot, but this is still a bit limited. Things might work better if the language for the recognized speech is assumed to be somewhat artificial to begin with. There are a lot of stereotypical phrases which the system could know to expect in various professional voice protocols like the ones doctors or aviators use. The system might also work with a computer command language that has a regular grammar by rejecting unparseable interpretations of the speech sounds, but that might not be very comfortable to use.