Domain-specific language models training methodology for the in-car infotainment

Abstract

The proposed paper focuses on the methodology for training small domain-specific language models. The methodology has been applied for creating a language model for the demonstration version of the in-vehicle infotainment system speech interface. The proposed methodology is based on constructing an initial n-gram language model only from the vocabulary. Such “random” model can be easily adapted to the user’s speaking style. The methodology is well suitable for situations when constructing the accurate deterministic grammar is not a trivial task or the higher flexibility is required. In the case of in-vehicle infotainment, the key idea is to decrease the cognitive load of the driver during controlling such a system and also to make designing process less time consuming. Evaluation of the proposed approach was done on the INCARSCOM corpus (Slovak In-car Command Database). Obtained results favour designed method instead of constructing the appropriate deterministic grammar.