Automatic Speech Recognition (ASR) is a complicated, multifaceted and fun(!) problem and practical solutions require two big pieces to come together. The firs

Automatic Speech Recognition (ASR) is a complicated, multifaceted and fun(!) problem and practical solutions require two big pieces to come together. The first is modeling. Both the latest and greatest in process models, like Hidden Markov Models (an oldie but goodie), that learn how to predict the likelihood of the next thing in a sequence given what things we've seen (usually a HMM only looks at the last one we've seen) and acoustic models which transform audio signals in ways that accentuate the characteristics of a speech signal that are most helpful in recognizing speech. OSS speech "toolkits" are often composed of the latest and greatest models of both types coming out of speech recognition literature. The second is data. Like any modeling project that makes used of supervised model training, you need labeled data and lots of it. The labeling must be correct, and labeling speech data is an extremely tedious and costly endeavor. This makes doing speech recognition in your garage difficult, as there is only one collaboratively constructed speech recognition data corpus that is free on the web and the quality is really not great. (It's unforgiving work, that must be correct and double checked. It must be consistent. You really need to pay for it.) So, when you look around the web and you see open source ASR toolkits, what you're really seeing is people passing around toolkits for people who develop ASRs to play with modeling. Taking one of these toolkits and turning it into a commercial quality functioning speech recognizer requires training it on good data and validating your results. While this could potentially be done openly and on the Internet, the costs involved in getting good labeled data has kept the production of really well functioning turnkey systems within the walls of corporate and academic entities. That said, it would be pretty awesome if Google went ahead and release a giant labeled corpus to the public. : )