On Combining Language Models : Oracle Approach 
 In this paper, we address the problem of combining several language models (LMs) . We find that simple interpolation methods , like log-linear and linear interpolation , improve the performance but fall short of the performance of an oracle . The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate ) from a list of word strings , where each word string has been obtained by using a different LM . Actually, the oracle acts like a dynamic combiner with hard decisions using the reference . We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further . We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree . The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence . 