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Robust parsing for word lattices in continuous speech recognition systems

Momtazi, S ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ISSPA.2007.4555313
  3. Publisher: 2007
  4. Abstract:
  5. One of the roles of a Natural Language Processing (NLP) model in Continuous Speech Recognition (CSR) systems is to find the best sentence hypothesis by ranking all n-best sentences according to the grammar. This paper describes a robust parsing algorithm for Spoken Language Recognition (SLR) which utilizes a technique that improves the efficiency of parsing. This technique integrates grammatical and statistical approaches, and by using a best-first parsing strategy improves the accuracy of recognition. Preliminary experimental results using a Persian continuous speech recognition system show effective improvements in accuracy with little change in recognition time. The word error rate was also reduced by 18%. ©2007 IEEE
  6. Keywords:
  7. Artificial intelligence ; Computational linguistics ; Computer networks ; Continuous speech recognition ; Error analysis ; Linguistics ; Signal processing ; Speech ; Speech analysis ; Continuous speech ; Robust parsing ; Speech recognition
  8. Source: 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Sharjah, 12 February 2007 through 15 February 2007 ; 2007 ; 1424407796 (ISBN); 9781424407798 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4555313