This paper focuses on dynamic topic identification for adaptive statistical language modeling in automatic speech recognition. It proposes a more correct solution of the cache model presented in from a mathematical point of view, which constitutes at the same time a simplification and an improvement of the model. Moreover, an original solution is put forward to overcome some limitations of this model. This new solution proposes to take into account the underlying semantic concepts of the text by introducing triggers of words into the cache memory. The relative identification accuracy is assessed on a newspaper corpus, highlighting a small increase of identification rate. In practice, the original cache model achieves an identification rate of 79.5%. Making use of the triggers, the feasibility of the task is investigated by an improvement of 1.2% in identification rate. This study is thus devoted to the specification of a possible direction to perform well dynamic topic identification for automatic speech recognition.