N-gram adaptation using Dirichlet class language model based on part-of-speech for speech recognition
Ali Hatami, Ahmad Akbari, Babak Nasersharif · 2013
Language model plays an important role in automatic speech recognition (ASR) systems. Performance of this model depends on its adaptation to the linguistic features. 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. This work proposes an idea for using syntactic information such as part-of-speech (POS) in DCLM for combining with an n-gram language model. In our proposed approach, word clustering is based on POS of previous words and history words. The performance of language models are evaluated on BijanKhan corpus using a hidden Markov model based ASR system. Our experiments show that using POS information along with history words and class of history words improves 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% in comparison to DCLM.