Optimization of string length for spoken digit input with error correction

doi:10.1016/S0020-7373(88)80061-0

International Journal of Man-Machine Studies

Volume 28, Issue 6, June 1988, Pages 573-581

https://doi.org/10.1016/S0020-7373(88)80061-0 Get rights and content

Abstract

No matter how much the performance of speech recognition systems improves, it is unlikely that perfect recognition will always be possible in practical situations. Environmental sounds will interfere with the recognition. In such circumstances it is sensible to provide feedback so that any errors which occur may be detected and corrected. In some situations, such as when the eyes are busy or over the telephone, it is necessary to provide feedback auditorily. This takes time, so the most efficient procedure should be determined. In the case of entering digits into a computer the question arises as to whether feedback should be provided after each digit has been spoken or after a string of digits has been recognized. It has been be found that this depends upon the accuracy of the recognizer and on the times required for recognizing the utterances and for changing from recognizing to synthesizing speech.

References (8)

W.A. Ainsworth
Audio feedback for error correction in a digit recognition task
J.L. Flanagan
T.B. Martin et al.
Practical speech recognizers and some performance effectiveness
J. Peckham
Human factors in speech recognition

There are more references available in the full text version of this article.

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Speech recognition technology continues to improve, but users still experience significant difficulty using the software to create and edit documents. The reported composition speed using speech software is only between 8 and 15 words per minute [Proc CHI 99 (1999) 568; Universal Access Inform Soc 1 (2001) 4], much lower than people's normal speaking speed of 125–150 words per minute. What causes the huge gap between natural speaking and composing using speech recognition? Is it possible to narrow the gap and make speech recognition more promising to users? In this paper we discuss users' learning processes and the difficulties they experience as related to continuous dictation tasks using state of the art Automatic Speech Recognition (ASR) software. Detailed data was collected for the first time on various aspects of the three activities involved in document composition tasks: dictation, navigation, and correction. The results indicate that navigation and error correction accounted for big chunk of the dictation task during the early stages of interaction. As users gained more experience, they became more efficient at dictation, navigation and error correction. However, the major improvements in productivity were due to dictation quality and the usage of navigation commands. These results provide insights regarding the factors that cause the gap between user expectation with speech recognition software and the reality of use, and how those factors changed with experience. Specific advice is given to researchers as to the most critical issues that must be addressed.
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Optimization of string length for spoken digit input with error correction

Abstract

Audio feedback for error correction in a digit recognition task

Practical speech recognizers and some performance effectiveness

Human factors in speech recognition