Analysing weaknesses of language models for speech recognition
J.P. Ueberla · 2002
We analyse the weaknesses of language models for speech recognition, in order to subsequently improve the models. First, a definition of a weakness of a probabilistic language model that is applicable to almost all currently used models is given. This definition is then applied to a class based bi-gram model. The results show that one can gain considerable insight into a model by analysing its weaknesses. Moreover, when the model was modified in order to avoid one of the weaknesses, the modeling of unknown words, the performance of the model improved significantly.