Human Factors and Voice Interactive Systems by John Karat, Jennifer Lai, Catalina Danis (auth.), Daryle
By John Karat, Jennifer Lai, Catalina Danis (auth.), Daryle Gardner-Bonneau (eds.)
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We did away with the alternate word list since it was not doing us any good. When the radio log ist electronically signed his or her report as fmal, the pro gram quickly scanned the report for new words and prompted the user to record a pronunciation if it found any words that needed to be added to the vocabulary. The ability to record a new pronunciation for a word that was often rnisrecognized for that user was relegated to the advanced function set. 22 Karat, Lai, Danis, and Wolf Feedback of State.
Thus, if one spoke like a typical New Yorker and was able -to produce sentences with a fluency comparable to that of read speech, one could achieve accuracy sirnilar to what was measured in our labs. This was not the case for many of our users. The next iteration of the software had a model based on 600 prototypes. As a result, the speech engine could recognize people speaking with a southern twang as easily as someone from Seattle or New England. This resulted in a dramatic increase in the accuracy of the engine between the start of the project and its finish.
For example, since we had to implement a set of cursor movement commands that only differed by the last syllable, there were often within-set rnisrecognitions. One caveat for this type of solution is that it can work reliably only with applications based on discrete speech recognition. In MedSpeak (to be discussed later), which uses continuous speech, hyphenated multi-word commands are not as easily distinguishable acoustically from text. One may be able to create commands that are highly unlikely to appear as sequences of text words, but this forces the language model alone to detect the anomalies, and loses the power of the acoustic processor.