MMI-MAP and MPE-MAP for acoustic model adaptation

This paper investigates the use of discriminative schemes based on the maximum mutual information (MMI) and minimum phone error (MPE) objective functions for both task and gender adaptation. A method for incorporating prior information into the discriminative training framework is described. If an appropriate form of prior distribution is used, then this may be implemented by simply altering the values of the counts used for parameter estimation. The prior distribution can be based around maximum likelihood parameter estimates, giving a technique known as I-smoothing, or for adaptation it can be based around a MAP estimate of the ML parameters, leading to MMI-MAP, or MPE-MAP. MMI-MAP is shown to be effective for task adaptation, where data from one task (Voicemail) is used to adapt a HMM set trained on another task (Switchboard). MPE-MAP is shown to be effective for generating gender-dependent models for Broadcast News transcription.