Parallel Structure in an Integrated Speech-Recognition Network
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Comparative experiments on large vocabulary speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
HLT '94 Proceedings of the workshop on Human Language Technology
Efficient backward decoding of high-order hidden Markov models
Pattern Recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Improving automatic speech recognizer of voice search using system combination
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
New uses for the N-best sentence hypotheses within the BYBLOS speech recognition system
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
The BBN/HARC spoken language understanding system
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Phonetic training and language modeling for word spotting
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
A new fast match for very large vocabulary continuous speech recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis
Expert Systems with Applications: An International Journal
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The authors introduce a technique that greatly speeds up expensive time-synchronous beam searches in speech recognition. The algorithm is called the forward-backward search and is mathematically related to the Baum-Welch forward-backward training algorithm. It uses a simplified forward pass followed by a detailed backward search. The information stored in the forward pass is used to decrease the computation in the backward pass by a large factor. An increase in speed of a factor of 40 with no increase in search errors was observed. The authors also describe how they have incorporated this algorithm into a real-time speaker-independent spoken language understanding system. One version of this is based on the 1000 word Resource Management vocabulary and is directed by a statistical class grammar. Another version has been incorporated into a military transportation planning application called DART (Dynamic Analysis Re-planning Tool).