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Using Context to Correct Phone Recognition Errors

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There are many circumstances in which it is useful or necessary to recognise phones rather than words, but phone recognition is inherently less accurate than word recognition. We describe here a post-recognition method for "translating" an errorful phone string output by a speech recogniser into a string that more closely matches the transcription. The technique owes something to Kohonen's idea of "dynamically expanding context" in that it learns from the errors made by the recogniser in a particular context, but it uses many contexts rather than a single context to estimate the "translation" of a recognised phone. The weights given to the different contexts in estimating the translation are determined discriminatively. On the WSJCAM0 database, the technique gives a 19.2% relative improvement in phone errors (including insertions) over the baseline, compared with a 6.2% improvement obtained using dynamically expanding context.

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