Two Approaches to Class-Based Language Models for ASR

In this work, we propose and formulate two different approaches for the language model employed in an Automatic Speech Recognition application. Both approaches make use of class-based language models, but taking into account that the classes are made up of segments or sequences of words. Experiments, carried out over a spontaneous dialogue corpus in Spanish, demonstrate the ability of the proposed models to learn the way in which the language is generated.