This study presents an N-gram adaptation technique when additional text data for the adaptation do not exist. We use a language modeling approach to the information retrieval (IR) technique to collect the appropriate adaptation corpus from baseline text data. We propose to use a dynamic interpolation coefficient to merge the N-gram, where the interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech. Experimental results show that the proposed adapted N-gram always has better performance than the background N-gram.

The matrix inequality condition has been considered as the main condition for the stability of RHC. But it is difficult to apply the matrix inequality condition for guaranteeing the stability of any physical system because of the high gain problem brought about the high value of the final state weighting matrix. Therefore, in this study, a new stability condition for RHC is proposed and it extends the range of the final state weighting matrix guaranteeing the stability of RHC in comparison with the case of the matrix inequality condition. The proposed stability condition is based not only on a final state weighting matrix but also on a horizon size and guarantees the stability for other forms of model predictive control just like the matrix inequality condition.