Long-Distance Continuous Space Language Modeling for Speech Recognition

The n-gram language models has been the most frequently used language model for a long time as they are easy to build models and require the minimum effort for integration in different NLP applications. Although of its popularity, n-gram models suffer from several drawbacks such as its ability to generalize for the unseen words in the training data, the adaptability to new domains, and the focus only on short distance word relations. To overcome the problems of the n-gram models the continuous parameter space LMs were introduced. In these models the words are treated as vectors of real numbers rather than of discrete entities. As a result, semantic relationships between the words could be quantified and can be integrated into the model. The infrequent words are modeled using the more frequent ones that are semantically similar. In this paper we present a long distance continuous language model based on a latent semantic analysis (LSA). In the LSA framework, the word-document co-occurrence matrix is commonly used to tell how many times a word occurs in a certain document. Also, the word-word co-occurrence matrix is used in many previous studies. In this research, we introduce a different representation for the text corpus, this by proposing long-distance word co-occurrence matrices. These matrices to represent the long range co-occurrences between different words on different distances in the corpus. By applying LSA to these matrices, words in the vocabulary are moved to the continuous vector space. We represent each word with a continuous vector that keeps the word order and position in the sentences. We use tied-mixture HMM modeling (TM-HMM) to robustly estimate the LM parameters and word probabilities. Experiments on the Arabic Gigaword corpus show improvements in the perplexity and the speech recognition results compared to the conventional n-gram.