language model adaptation using dirichlet class language model based on part-of-speech

Language modeling has many applications in a large variety of domains. Performance of this model depends on its adaptation to a particular style of data. Accordingly, adaptation methods endeavour to apply syntactic and semantic characteristics of the language for language modeling. The previous adaptation methods such as family of Dirichlet class language model (DCLM) extract class of history words. These methods due to lake of syntactic information are not suitable for high morphology languages such as Farsi. In this paper, we present an idea for using syntactic information such as part-of-speech (POS) in DCLM for combining with one of the language models of n-gram family. In our work, word clustering is based on POS of previous words and history words in DCLM. The performance of language models are evaluated on BijanKhan corpus using a hidden Markov model based ASR system. The results show that use of POS information along with history words and class of history words improves performance of language model, and decreases the perplexity on our corpus. Exploiting POS information along with DCLM, the word error rate of the ASR system decreases by 1.2% compared to DCLM.

Keywords:

Speech Recognition , Language Model Adaptation , Part-of-Speech , Perplexity , Word Error Rate