Discriminative training of HMMs for automatic speech recognition: A survey

Abstract

Recently, discriminative training (DT) methods have achieved tremendous progress in automatic speech recognition (ASR). In this survey article, all mainstream DT methods in speech recognition are reviewed from both theoretical and practical perspectives. From the theoretical aspect, many effective discriminative learning criteria in ASR are first introduced and then a unifying view is presented to elucidate the relationship among these popular DT criteria originally proposed from different viewpoints. Next, some key optimization methods used to optimize these criteria are summarized and their convergence properties are discussed. Moreover, as some recent advances, a novel discriminative learning framework is introduced as a general scheme to formulate discriminative training of HMMs for ASR, from which a variety of new DT methods can be developed. In addition, some important implementation issues regarding how to conduct DT for large vocabulary ASR are also discussed from a more practical aspect, such as efficient implementation of discriminative training on word graphs and effective optimization of complex DT objective functions in high-dimensionality space, and so on. Finally, this paper is summarized and concluded with some possible future research directions for this area. As a technical survey, all DT techniques and ideas are reviewed and discussed in this paper from high level without involving too much technical detail and experimental result.