A unification-grammar-directed one-pass search algorithm for parsing spoken language

The author presents an efficient parsing algorithm for integrating the search problems both in speech and language processing in general use for spoken language systems (SLSs). The parsing algorithm is an extension of the finite-state-network (FSN)-directed, one-pass search algorithm to one directed by a context-free grammar or a unification-based grammar with retention of the frame-synchronous procedure. This integrating algorithm can approximately find a global optimal sentence hypothesis, and it avoids the overhead of hierarchical systems based on the lattice parsing approach. The computational complexity of this algorithm is proportional to the length of the input utterance. As the search process in speech recognition can directly account for the predictive information, this framework can be extended to the SLS to deal with dynamically varying constraints in a dialogue situation.<<ETX>>

[1]  Frederick Jelinek,et al.  A real-time, isolated-word, speech recognition system for dictation transcription , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  M. Tomita,et al.  An efficient word lattice parsing algorithm for continuous speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Stuart M. Shieber,et al.  Using Restriction to Extend Parsing Algorithms for Complex-Feature-Based Formalisms , 1985, ACL.

[4]  Hy Murveit,et al.  Integrating natural language constraints into HMM-based speech recognition , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[5]  Michael D. Brown,et al.  An algorithm for connected word recognition , 1982, ICASSP.

[6]  John Makhoul,et al.  BYBLOS: The BBN continuous speech recognition system , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Mei-Yuh Hwang,et al.  The SPHINX speech recognition system , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[8]  Chin-Hui Lee,et al.  A network-based frame-synchronous level building algorithm for connected word recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[9]  R. Schwartz,et al.  The N-best algorithms: an efficient and exact procedure for finding the N most likely sentence hypotheses , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[10]  Andreas Noll,et al.  A data-driven organization of the dynamic programming beam search for continuous speech recognition , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Stephen E. Levinson,et al.  Speaker independent connected word recognition using a syntax-directed dynamic programming procedure , 1982 .

[12]  Hermann Ney,et al.  Dynamic programming speech recognition using a context-free grammar , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.