Document-based Dirichlet class language model for speech recognition using document-based n-gram events

We propose a document-based Dirichlet class language model (DDCLM) for speech recognition using document-based n-gram events. In this model, the class is conditioned on the immediate history context and the document, and the word is conditioned on the the class and the document in the original DCLM model. In the DCLM model, the class information was obtained from the (n-1) history words of n-gram events of a training corpus. Here, the model uses the count of the n-grams, which are the number of appearances of the n-grams in the corpus. These counts are the sum of the n-gram counts in different documents where they could appear to describe different topics. Therefore, the n-gram counts of the corpus may not yield the proper class information for the histories. We encounter this problem in the DCLM model and propose a DDCLM model that overcomes the above problem by finding the class information for the document-based history context using the document-based n-gram events. We carried out experiments on a continuous speech recognition (CSR) task using the Wall Street Journal (WSJ) corpus and have seen that the proposed approach shows significant perplexity and word error rate (WER) reductions over the other approach.