Evaluation of Proposed Modifications to MPE for Large Scale Discriminative Training.
ICASSP(2007)
摘要
Minimum Phone Error (MPE) is an objective function for discriminative training of acoustic models for speech recognition. Recently several different objective functions related to MPE have been proposed. In this paper we compare implementations of three of these to MPE on English and Arabic broadcast news. The techniques investigated are Minimum Phone Frame Error (MPFE), Minimum Divergence (MD), and a physical-state level version of Minimum Bayes Risk which we call s-MBR. In the case of MPFE we observe improvements over MPE. We propose that the smoothing constant used in MPE should be scaled according to the average value of the counts in the statistics obtained from these objective functions.
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关键词
Bayes methods,speech recognition,Arabic broadcast news,English,MPE,acoustic models,large scale discriminative training,minimum Bayes risk,minimum divergence,minimum phone error,minimum phone frame error,smoothing constant,speech recognition,Discriminative Training,Minimum Bayes Risk,Minimum Divergence,Minimum Phone Error,Minimum Phone Frame Error
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