Multiclass composite N-gram language model based on connection direction

The authors propose a method to generate a compact, highly reliable language model for speech recognition based on the efficient classification of words. In this method, the connectedness with the words immediately before and after the word is taken to represent separate attributes, and individual classification is performed for each word. The resulting composite word class is created separately based on the distribution of words connected before or after. As a result, classification of classes is efficient and reliable. In a multiclass composite N-gram, which uses the proposed method for the variable-order N-gram to bring in chain words, the entry size is reduced to one-tenth, and the word recognition rate is higher than that of a conventional composite N-gram for particles or variable-length word arrays. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(7): 108–114, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1210

[1]  Haizhou Li,et al.  Building class-based language models with contextual statistics , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[2]  Yoshinori Sagisaka,et al.  Spontaneous dialogue speech recognition using cross-word context constrained word graphs , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[3]  Robert L. Mercer,et al.  Class-Based n-gram Models of Natural Language , 1992, CL.

[4]  Mari Ostendorf,et al.  HMM topology design using maximum likelihood successive state splitting , 1997, Comput. Speech Lang..

[5]  Yoshinori Sagisaka,et al.  Variable-order N-gram generation by word-class splitting and consecutive word grouping , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.