Context Dependent Class Language Model based on Word Co-occurrence Matrix in LSA Framework for Speech Recognition
نویسندگان
چکیده
We address the issue of data sparseness problem in language model (LM). Using class LM is one way to avoid this problem. In class LM, infrequent words are supported by more frequent words in the same class. This paper investigates a class LM based on LSA. A word-document matrix is usually used to represent a corpus in LSA framework. However, this matrix ignores word order in the sentence. We propose several word co-occurrence matrices that keep word order. Together with these matrices, we define a context dependent class (CDC) LM which distinguishes classes according to their context in the sentences. Experiments on Wall Street Journal (WSJ) corpus show that the word co-occurrence matrix works better than word-document matrix. Furthermore, the CDC achieves better perplexity than the traditional class LM based on LSA. Key–Words: LSA, Language model, Word co-occurrence matrix
منابع مشابه
Word Co-occurrence Matrix and Context Dependent Class in LSA based Language Model for Speech Recognition
A data sparseness problem for modeling a language often occurs in many language models (LMs). This problem is caused by the insufficiency of training data, which in turn, makes the infrequent words have unreliable probability. Mapping from words into classes gives the infrequent words more confident probability, because they can rely on other more frequent words in the same class. In this resea...
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