Search Strategies For Large-Vocabulary Continuous-Speech Recognition

This paper gives an overview of search strategies for large-vocabulary, continuous-speech recognition. We describe the adaptation of the one-pass beam search strategy to large vocabulary recognition. In the second part, we review the one-pass algorithm from the viewpoint of how the search space is organized and how duplication of partial hypotheses can be avoided. Thus we obtain natural variants of the conventional one-pass strategy: time conditioned copies vs. word conditioned copies and lattice vs. one-pass methods.

[1]  Hermann Ney,et al.  Continuous-speech recognition using a stochastic language model , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[2]  Hermann Ney,et al.  Data driven search organization for continuous speech recognition , 1992, IEEE Trans. Signal Process..

[3]  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.

[4]  Lalit R. Bahl,et al.  A fast approximate acoustic match for large vocabulary speech recognition , 1989, IEEE Trans. Speech Audio Process..

[5]  T. K. Vintsyuk Element-wise recognition of continuous speech composed of words from a specified dictionary , 1971, CYBERNETICS.

[6]  Frank K. Soong,et al.  A Tree.Trellis Based Fast Search for Finding the N Best Sentence Hypotheses in Continuous Speech Recognition , 1990, HLT.

[7]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[8]  Pietro Laface,et al.  Using grammars in forward and backward search , 1993, EUROSPEECH.

[9]  Hermann Ney,et al.  Word graphs: an efficient interface between continuous-speech recognition and language understanding , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  R. Schwartz,et al.  A comparison of several approximate algorithms for finding multiple (N-best) sentence hypotheses , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[11]  Aaron E. Rosenberg,et al.  Improved acoustic modeling for speaker independent large vocabulary continuous speech recognition , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[12]  James K. Baker,et al.  Stochastic modeling for automatic speech understanding , 1990 .

[13]  H. Sakoe,et al.  Two-level DP-matching--A dynamic programming-based pattern matching algorithm for connected word recognition , 1979 .

[14]  H. Ney,et al.  Improvements in beam search for 10000-word continuous speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  Francisco Casacuberta,et al.  An improvement of the two-level DP matching algorithm using k-NN techniques for acoustic-phonetic decoding , 1993, EUROSPEECH.

[16]  H. Sakoe,et al.  Two-level DP-matching algorithm-a dynamic programming based pattern matching algorithm for continuous speech recognition , 1979 .

[17]  Hermann Ney,et al.  The use of a one-stage dynamic programming algorithm for connected word recognition , 1984 .

[18]  Lalit R. Bahl,et al.  A Maximum Likelihood Approach to Continuous Speech Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.