Error Approximation and Minimum Phone Error Acoustic Model Estimation
Matthew Gibson, Thomas Hain · IEEE Transactions on Audio Speech and Language Processing · 2009
Minimum phone error (MPE) acoustic parameter estimation involves calculation of edit distances (errors) between correct and incorrect hypotheses. In the context of large-vocabulary continuous-speech recognition, this error calculation becomes prohibitively expensive and so errors are approximated. This paper introduces a novel error approximation technique. Analysis shows that this approximation yields a higher correlation to the Levenshtein error metric than a previously used approximation. Experimental evaluations on a large-vocabulary recognition task demonstrate that the novel approximation also delivers significant performance improvements over the previously used approximation when applied to MPE acoustic model estimation.