Discriminative training for segmental minimum Bayes risk decoding

Vlasios Doumpiotis, Stavros Tsakalidis, William Byrne

Abstract: A modeling approach is presented that incorporates discriminative training procedures within segmental minimum Bayes-risk decoding (SMBR). SMBR is used to segment lattices produced by a general automatic speech recognition (ASR) system into sequences of separate decision problems involving small sets of confusable words. Acoustic models specialized to discriminate between the competing words in these classes are then applied in subsequent SMBR rescoring passes. Refinement of the search space that allows the use of specialized discriminative models is shown to be an improvement over rescoring with conventionally trained discriminative models.