Maestro Recognition Server Cracked
say "open this video " + target(url)
maestro recognition server cracked
now that command launches and runs fine in the elcapitan development version. but in the older versions, i was getting the following error:
aol user interface: error -6
maestro recognition server cracked
the -6 is a generic speech recognition error code but it means that the url could not be specified properly to the speech recognition engine. for example, if the url begins with http, spaces, or reserved characters, it will fail with this error. in the past, i have had to take specific steps to correct the problem. for example, if you were using an outside url with spaces, say for a javascript url, you would use the text command:
say "open this video " + target(url) + " "
maestro recognition server cracked
now, if the outside url contained spaces, say for a javascript, it would correctly launch the app. alternately, you can use the say command to say nothing or if you have a text string that begins with a url and spaces, use the following:
say "open this video " + target(url) + " space"
maestro recognition server cracked
however, none of these fix the buffer overflow problem. someone suggested a fix in the past that i have not tried. in the meantime, i have used the macspeech engine to work around this problem in the past. for example,
say "open this video " + target(url)
maestro recognition server cracked
invokes the macspeech engine. you get the speech recognition dialog box to launch the app. but you can skip that if you have the app installed. as you get older, learning technology become more important. as a teenager, i had a mac with a 386slc, 3 mb of ram, and a 12 inch apple display with no hard drive. it was my first mac and i did not know any better. when i bought my first mac, it had 68020 processor and 80 mbytes of memory. i tried to buy speech recognition for that computer but had no luck. i later got a mac with a 3.25 gb hard drive, 4 mb of ram, and a 80386 processor. i finally got speech recognition for that mac but i got it from a school to learn programming with. a few years later, i got a mac with a 4.5 gb hard drive, 20 mb of ram, and an 8 mhz 68030 processor. i finally got speech recognition for that mac and was able to teach myself to program in it. but i never learned any other more sophisticated computing platforms including windows, linux, or any of the unix platforms.
meanwhile, i had been experimenting with text-based speech recognition in other products like dragon (which i still used). theyre much more powerful than nuance and dragon, but they had some of the same limitations. you cannot drive the user interface and have it respond, for example. for years i continued to work on improving the accuracy of speech recognition for my own company, articulate. i had some success, but it took me several years to resolve a variety of issues in speech-recognition, such as noise, digit span, known sentences. we finally released dragon 2004 at macworld when i was editor-in-chief and won apple editors choice honors. i used the dragon speech recognition engine in my successor voice-enabled products, tapback and hands-free.
dragon 2.0 had a lot of the right ideas and many of the problems had been solved. it could drive the user interface, for example, and was tuned for digit recognition. in 2005, nuance took an interest in the macintosh product line and acquired dragon. we sold the tapback platform shortly after the acquisition.
the macspeech speech recognition engine was already over ten years old and had been acquired by a competitor a few years before. it was slow, had many limitations, and was poorly tuned for speech. it was far from state-of-the-art. nuance brought in a team of some of the best researchers and engineers in the business. they split the old team into product teams, each tasked with solving problems. i led the systems team. they innovated in many areas, including software-generated audio, acoustic models, client-side digit recognition, recognition latency, acoustic normalization, and much more. i spent time driving the design of the software-generated audio and the acoustic models. the results were impressive and provided us with the foundation to develop improved speech recognition in the years that followed, but the innovation was slow and they started from scratch. the benefits were finally clear to me about five years ago.
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