A New Word Clustering Method for Building N-Gram Language Models in Continuous Speech Recognition Systems

Abstract

In this paper a new method for automatic word clustering is presented. We used this method for building n-gram language models for continuous speech recognition (CSR) systems. In this method, each word is specified by a feature vector that represents the statistics of parts of speech (POS) of that word. The feature vectors are clustered by k-means algorithm. Using this method causes a reduction in time complexity which is a defect in other automatic clustering methods. Also, the problem of high perplexity in manual clustering methods is abated. The experimental results are based on Persian Text Corpus which contains about 9 million words. The extracted language models are evaluated by the perplexity criterion and the results show that a considerable reduction in perplexity has been achieved. Also reduction in word error rate of CSR system is about 16% compared with a manual clustering method.