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On the Benefits of Confidence Visualization in Speech Recognition

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If you have a question about this talk, please contact Philip Sterne.

In a typical speech dictation interface, the recognizer’s best-guess is displayed as normal, unannotated text. This ignores potentially useful information about the recognizer’s confidence in its recognition hypothesis. Using a confidence measure (which itself may sometimes be inaccurate), we investigated providing visual feedback about low-confidence portions of the recognition using shaded, red underlining. An evaluation showed, compared to a baseline without underlining, underlining low-confidence areas did not increase user’s speed or accuracy in detecting errors. However, we found that when recognition errors were correctly underlined, they were discovered significantly more often than baseline. Conversely, when errors failed to be underlined, they were discovered less often. Our results indicate confidence visualization can be effective – but only if the confidence measure has high accuracy. Further, since our results show that users tend to trust confidence visualization, designers should be careful in its application if a high accuracy confidence measure is not available.

The paper and videos can be found here

Even more details can be found in chapter 2 of my thesis

This will be a 10-minute CHI practice talk.

This talk is part of the Machine Learning Journal Club series.

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