Abstract
In this paper, we introduce a document-specific context probabilistic latent semantic analysis (DCPLSA) model for speech recognition. This is an extension of a CPLSA model [1] where the probability of word is conditioned only on topics. The CPLSA model uses the bigram counts that are the number of appearances of the bigrams in the corpus. These counts are the sum of the bigram counts in different documents where they could appear to describe different topics. We encounter this problem in the CPLSA model and introduce the document-specific CPLSA model (DCPLSA) where the probability of a word is conditioned on both topic and document. We carried out experiments on a continuous speech recognition (CSR) task using the Wall Street Journal (WSJ) corpus and have seen that the proposed DCPLSA approach yields significant reduction in both perplexity and word error rate (WER) measurements over the other approaches used in the literature.
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