الفهرس 
Long - distance continuous language modeling for speech recognition
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Abstract
This thesis deals with the problem of building continuous language models for automatic continuous speech recognition systems. 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 study 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