Modified MMI/MPE: a direct evaluation of the margin in speech recognition

In this paper we show how common speech recognition training criteria such as the Minimum Phone Error criterion or the Maximum Mutual Information criterion can be extended to incorporate a margin term. Different margin-based training algorithms have been proposed to refine existing training algorithms for general machine learning problems. However, for speech recognition, some special problems have to be addressed and all approaches proposed either lack practical applicability or the inclusion of a margin term enforces significant changes to the underlying model, e.g. the optimization algorithm, the loss function, or the parameterization of the model. In our approach, the conventional training criteria are modified to incorporate a margin term. This allows us to do large-margin training in speech recognition using the same efficient algorithms for accumulation and optimization and to use the same software as for conventional discriminative training. We show that the proposed criteria ...

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Georg Heigold, Hermann Ney, Ralf Schlüter, Thomas Deselaers
• Keywords: Conventional Training Criteria, ICML 2008, Machine Learning, Margin Term, Recognition Training Criteria