  In Flanders, all TV shows are subtitled. However, the process of subtitling
is a very time-consuming one and can be sped up by providing the output of a
speech recognizer run on the audio of the TV show, prior to the subtitling.
Naturally, this speech recognition will perform much better if the employed
language model is adapted to the register and the topic of the program. We
present several language models trained on subtitles of television shows
provided by the Flemish public-service broadcaster VRT. This data was gathered
in the context of the project STON which has as purpose to facilitate the
process of subtitling TV shows. One model is trained on all available data (46M
word tokens), but we also trained models on a specific type of TV show or
domain/topic. Language models of spoken language are quite rare due to the lack
of training data. The size of this corpus is relatively large for a corpus of
spoken language (compare with e.g. CGN which has 9M words), but still rather
small for a language model. Thus, in practice it is advised to interpolate
these models with a large background language model trained on written
language. The models can be freely downloaded on
http://www.esat.kuleuven.be/psi/spraak/downloads/.
