Application Research On Statistical Language Model Of Large Vocabulary Continuous Speech Recognition System

With the widely application of speech recognition technology, which will be a very important topic in pattern recognition fields, because its development will deeply influence the future of life and work, and also it will become the main technology in next ten years. Due to large vocabulary, speaker independent and continuous speech recognition system is one of the most difficult and challenging topics Among speech recognition technologies in recent years. So more and more institutes and coporations have taken part in research and development of the technology. Although large vocabulary continuous speech recognition (LVCSR) systems based on Chinese have been quickly developed in recent years, none of them is good enough for real application. So, further research in this area is necessary and significant.The language model is extremely important in LVCSR, Its performance directly affects the application ranges and effect of the whole speech recognition system. So building and updating a reliable language model is very important for speech recognition system.Firstly, the working principle, smooth technique, evaluation criterion and related principle of language model are discussed in detail in this thesis. Secondly, the corpus is built to meet the requirement of HLM (HTK language modeling) tool in the HTK (Hidden markov toolkit), through seting up the HTK modeling platform under Linux, and using Linux commands and programming scripts programs with Bash and Perl to preprocess and divide the original data. Thirdly, a Tri-gram language model is created by using HLM to train the data in corpus continuously and to evaluate the results. Fourthly, with the continuous change of the recognition contents, the performance of language model will get down and many out of vocabulary in speech recognition appear. So, a whole solution is put forward to automatically update language model by making use of HTML Parser and PDFBox develop toolkits to extract web content and PDF document as a data source, which can improve the performance of language model.In the end, the framework of LVCSR system is introduced, and the recognition effect of the language model built in this thesis is validated through an experiment in a speech recognition system. Because the recognition result may not be completely right, and an approach that can modify and adjust the contents and time of the recognition result is discussed, which can keep the consistency between the recognition result and the speech, and improve the correctness of speech recognition.