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I have been doing a great deal of writing lately, though obviously not here. I thought that perhaps at some point in graduate school, I should try getting some of the projects I have done published, and I thought that time was now. Since this requires writing them up, I have been writing. I have gotten a lot of writing done, but I noticed that this came with an increased number of hours spent sitting at my computer. Knowing enough friends who have suffered from repetitive stress injuries, I decided I should take a proactive approach to ergonomics.
One outcome of this process is was that I purchased voice-recognition software, namely Dragon Dictate. This actually complements my preference to pace while I think. My writing style involves a lot of thinking, punctuated by occasional bursts of typing. So being able to write as I pace seemed like a good idea.
I cannot say that this experiment has been an overwhelming success. Based on what I have learned from the documentation, Dragon Dictate seems to place a great deal of faith in transitional probabilities. That is, the hypotheses it makes about what you are saying are based not only on the sounds that you make but based on what words typically come after one another.
Of course, what words typically follow one another depends a great deal on what you are talking about. I suspect that Dragon Dictate was not trained on a corpus involving a great deal of psycholinguistics papers, but in fact it is psycholinguistics papers that I am writing. Dragon Dictate makes a number of very systematic and very annoying errors. For instance, it is absolutely convinced that, no matter how carefully I say the word “verb”, I could not possibly have meant to say that word, and probably meant "four herbs" or some such. In the general case, this is probably the right conclusion. The word “verb” is so rarely spoken, that it is probably a good bet that it even if you think you heard the word “verb”, what was actually spoken was probably something else. However, since almost all my papers are about verbs, I use that word so often that probably the right hypothesis is that no matter what you think you heard, the word I actually uttered was “verb”.
Needless to say, it doesn't do very well with technical terms from semantic and syntactic theory, either.
The upshot is that I spend so much time correcting DragonDictate's mistakes, that it is not clear that I wouldn't be better off just typing the document begin with (you can correct using voice commands, but it is so cumbersome that I usually type instead). Dragon Dictate has a function where you can feed it various documents. The documentation appears to imply that it can learn the relevant word frequencies and transitional probabilities from these documents. I have been feeding at papers I have written, in the hopes that this will help out. So far there has been limited improvement, but I am not sure just how large a corpus of needs. I will keep you updated.
(Written using DragonDictate plus hand correction.)
One outcome of this process is was that I purchased voice-recognition software, namely Dragon Dictate. This actually complements my preference to pace while I think. My writing style involves a lot of thinking, punctuated by occasional bursts of typing. So being able to write as I pace seemed like a good idea.
I cannot say that this experiment has been an overwhelming success. Based on what I have learned from the documentation, Dragon Dictate seems to place a great deal of faith in transitional probabilities. That is, the hypotheses it makes about what you are saying are based not only on the sounds that you make but based on what words typically come after one another.
Of course, what words typically follow one another depends a great deal on what you are talking about. I suspect that Dragon Dictate was not trained on a corpus involving a great deal of psycholinguistics papers, but in fact it is psycholinguistics papers that I am writing. Dragon Dictate makes a number of very systematic and very annoying errors. For instance, it is absolutely convinced that, no matter how carefully I say the word “verb”, I could not possibly have meant to say that word, and probably meant "four herbs" or some such. In the general case, this is probably the right conclusion. The word “verb” is so rarely spoken, that it is probably a good bet that it even if you think you heard the word “verb”, what was actually spoken was probably something else. However, since almost all my papers are about verbs, I use that word so often that probably the right hypothesis is that no matter what you think you heard, the word I actually uttered was “verb”.
Needless to say, it doesn't do very well with technical terms from semantic and syntactic theory, either.
The upshot is that I spend so much time correcting DragonDictate's mistakes, that it is not clear that I wouldn't be better off just typing the document begin with (you can correct using voice commands, but it is so cumbersome that I usually type instead). Dragon Dictate has a function where you can feed it various documents. The documentation appears to imply that it can learn the relevant word frequencies and transitional probabilities from these documents. I have been feeding at papers I have written, in the hopes that this will help out. So far there has been limited improvement, but I am not sure just how large a corpus of needs. I will keep you updated.
(Written using DragonDictate plus hand correction.)
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