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Automatic Selection of Recognition Errors by Respeaking the Intended Text

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We investigate how to automatically align spoken corrections with an initial speech recognition result. Such automatic alignment would enable one-step voice-only correction in which users simply respeak their intended text. We present three new models for automatically aligning corrections: a 1-best model, a word confusion network model, and a revision model. The revision model allows users to alter what they intended to write even when the initial recognition was completely correct. We evaluate our models with data gathered from two user studies. We show that providing just a single correct word of context dramatically improves alignment success from 65% to 84%. We find that a majority of users provide such context without being explicitly instructed to do so. We find that the revision model is superior when users modify words in their initial recognition, improving alignment success from 73% to 83%. We show how our models can easily incorporate prior information about correction location and we show that such information aids alignment success. Last, we observe that users speak their intended text faster and with fewer re-recordings than if they are forced to speak misrecognized text.

The paper is available here: http://www.keithv.com/pub/autoselect/

This talk is part of the Inference Group series.

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