1. A method comprising: accessing a corpus of terms by a parser using a processor in each of a plurality of speech applications; parsing, using said parser and said processor, said corpus of terms in each speech application to produce a plurality of first output sets, in which expressions identified in the corpus are replaced with corresponding grammar tags from a grammar that is specific to the application, wherein said grammar tags are selected from among command grammar tags and collection grammar tags; accessing said plurality of first output sets by a class-relabeler and said processor; replacing by the class-relabeler and said processor, for each of the plurality of speech applications, each of the grammar tags in the plurality of first output sets with a class identifier of an application-generic class, to produce plurality of a second output sets; accessing said plurality of second output sets by a token selector and said processor; processing collectively, by said token selector and said processor, the plurality of second output sets or data derived from the output sets with a statistical language model (SLM) trainer; and generating, using said processor, an application-generic class-based SLM using a set of results from said SLM trainer.