New results with the Lincoln tied-mixture HMM CSR system
HLT '91 Proceedings of the workshop on Speech and Natural Language
A rapid match algorithm for continuous speech recognition
HLT '90 Proceedings of the workshop on Speech and Natural Language
A CSR-NL interface specification version 1.5
HLT '89 Proceedings of the workshop on Speech and Natural Language
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
The Lincoln tied-mixture HMM continuous speech recognizer
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
A*-admissible heuristics for rapid lexical access
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Algorithms for an optimal A* search and linearizing the search in the stack decoder
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
The forward-backward search algorithm
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
A comparison of several approximate algorithms for finding multiple (N-best) sentence hypotheses
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Integration of speech recognition and natural language processing in the MIT VOYAGER system
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Fast sequential decoding algorithm using a stack
IBM Journal of Research and Development
The Lincoln large-vocabulary stack-decoder HMM CSR
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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The stack decoder is an attractive algorithm for controlling the acoustic and language model matching in a continuous speech recognizer. A previous paper described a near-optimal admissible Viterbi A * search algorithm for use with noncrossword acoustic models and no-grammar language models [16]. This paper extends this algorithm to include unigram language models and describes a modified version of the algorithm which includes the full (forward) decoder, cross-word acoustic models and longer-span language models. The resultant algorithm is not admissible, but has been demonstrated to have a low probability of search error and to be very efficient