Abstract
In this paper a new method for automatic word clustering is presented. We used this method for building n-gram language models for Persian 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.
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Bahrani, M., Sameti, H., Hafezi, N., Momtazi, S. (2008). A New Word Clustering Method for Building N-Gram Language Models in Continuous Speech Recognition Systems. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_30
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DOI: https://doi.org/10.1007/978-3-540-69052-8_30
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