A language processing method based on a statistical model is proposed and studied. This method is different from conventional bigram model, all the words in the vocabulary are mapped into several equivalence classes, according to the collocation between adjacent words. This modified bigram approach retains the simplicity and effectiveness of the bigram model, and also has the advantage of reducing the requirement of memory size to make this approach realizable on a PC computer. It also can moderate the problem of zero probabilities. All the parameters of the model are estimated from a corpus of 1.6 million words, covering a lexicon of 30,000 words. The training of the modified model is automatically realized with an unsupervised learning procedure. Several tests for decoding Chinese syllable strings to text have been carried out. The test results show that the average words correct rate is 87%. For news reports, a high word correct rate 96% is reached based on this modified bigram model.<>


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    Title :

    Language processing for Chinese speech recognition


    Contributors:
    Huang, T. (author) / Jiang, Y. (author)


    Publication date :

    1994-01-01


    Size :

    254626 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Language Processing for Chinese Speech Recognition

    Huang, T. / Jiang, Y. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994


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