Implementation of Speech Recognition Software for Text Processing

In recent years, information technology has advanced at an incredible pace. One new technology that has recently become available to the average computer user is speech recognition software for text processing. The rationale behind implementing such new technologies is often to gain productivity improvements associated with the substitution of machinery for labor. However, the literature shows little direct evidence of a positive relationship between information technology investment and subsequent productivity benefits. This thesis reports on the examination into the productivity implications of implementing speech recognition software in a text-processing environment. More specifically, research was conducted to compare text processing speeds and error rates using speech recognition software versus the keyboard and mouse. Of interest was the time required to input and proofread text processing tasks as well as the number of errors generated using both methods of text input. The empirical data offer somewhat mixed results. While users initially entered text faster using speech recognition software (p greater than .05), they generated more errors and consequently performed proofreading and error corrections slower using speech. These results suggest that, in terms of accurate text processing, speech recognition software is still not a practical alternative to the keyboard. Therefore, implementation of speech recognition software is unlikely to result in any gains in productivity that would serve to justify its cost.