Comparison of a bigram PLSA and a novel context-based PLSA language model for speech recognition

We propose a novel context-based probabilistic latent semantic analysis (PLSA) language model for speech recognition. In this model, the topic is conditioned on the immediate history context and the document in the original PLSA model. This allows computing all the possible bigram probabilities of the seen history context using the model. It properly computes the topic probability of an unseen document for each history context present in the document. We compare our approach with a recently proposed unsmoothed bigram PLSA model where only the seen bigram probabilities are calculated, which causes computing the incorrect topic probability for the present history context of the unseen document. The proposed model requires a significantly less amount of computation time and memory space requirements than the unsmoothed bigram PLSA model. We carried out experiments on a continuous speech recognition (CSR) task using theWall Street Journal (WSJ) corpus. The proposed approach shows significant reduction in both perplexity and word error rate (WER) measurements over the other approach.