Margin based discriminative training techniques for automatic speech recognition.

Abstract

Recently, large margin techniques have gained popularity for discriminative training of continuous-density hidden Markov models. These techniques are motivated by statistical learning theory, aiming to strike a balance between model complexity and generalization error. When used in speech recognition, these techniques separate discriminant scores from the correct transcriptions from all incorrect recognizer outputs. The parameters of acoustic models are adjusted to maximize the separation—margin—that is proportional to recognition errors. Many of such techniques have been successfully applied to large vocabulary speech recognition tasks and have improved the performance of existing systems with traditional approaches for parameter estimation. In this talk, I will review some of the work that my collaborators and I have undertaken in this direction. I will also describe a few new advances, including online learning of large margin separation as well as applying margin based techniques to adaptively transform acoustic features jointly with acoustic models.