Posterior-Scaled MPE: Novel Discriminative Training Criteria

We recently discovered novel discriminative training criteria following a principled approach. In this approach training criteria are developed from error bounds on the global error for pattern classification tasks that depend on non-trivial loss functions. Automatic speech recognition (ASR) is a prominent example for such a task depending on the non-trivial Levenshtein loss. In this context, the posterior-scaled Minimum Phoneme Error (MPE) training criterion, which is the state-of-the-art discriminative training criterion in ASR, was shown to be an approximation to one of the novel criteria. Here, we describe the implementation of the posterior-scaled MPE criterion in a transducer-based framework, and compare this criterion to other discriminative training criteria on an ASR task. This comparison indicates that the posterior-scaled MPE criterion performs better than other discriminative criteria including MPE.