Lexical tuning based on triphone confidence estimation

We propose and test a practical means of finding poor pronunciations and missing variants for large lexicons. We do so by statistically assessing the confidence of each phone in each pronunciation and comparing it with the statistical distribution of the same confidence metric for corresponding phones over the entire training corpus. A phone is targeted for correction for each word in which its mean score is significantly less than the phone's mean score over the entire training corpus. Neighboring phones are also reviewed for their contribution to the target phone's poor score. Thus far, we have experimented with this technique by manually correcting the pronunciation. In experiments with Wall Street Journal and dictated physical examination corpora, word error rates were reduced commensurate with the number of dictionary entries whose pronunciations were corrected as result of this process.