The most important things I've come up with while researching this area is the following. The error recovery process on a failure in the system is simply to ba

The most important things I've come up with while researching this area is the following. The error recovery process on a failure in the system is simply to bad. It takes quite a long time to correct an error in speec recognition. So the idea is this that a high error rate is actually acceptable if the recoery delay is small. So while typing on a keyboard the error rate is quite high yet the user doesn't notice since to fix the error is not that painstaking. The same can not be said for speech recognition. As such i feel that speech recognition would need a better recovery process. How and why exactly is beyond me however the Dasher project have some ideas on this by using a hybrid system. For the fully blind however this is a non option. The next point that I'd like to make is that the most commmon source of errors seems to come from where the prediction algorithm have two or three probable alternatives for a word. In the case of predicting data in a narrow scope such as a programming language this is greatly mitigated. To keep these ambiguity errors to their bare minimum the speech recognizer ought to be fed the alternatives that are possible. This can be quite easily be taken from the AST and is stricly defined as the syntax is much more restricted than commonly spoken languages. I am currently trying to compile a list of alternative input methods. As such I find your work really interestingg. I intened to do a chapter on speech recognition soon and would love to have your feedback on it. In conjunction with this i want to make a resource of boilerplate code for controlling various operating system and to have a signle resource for state of the art prediction models. If you are interested please don't hesitate to contact me at: trbefr@protonmail.com