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
The application of hidden Markov models in speech recognition
Foundations and Trends in Signal Processing
Sequentially finding the N-best list in hidden Markov models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Burst detection from multiple data streams: a network-based approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Speech recognition using segmental neural nets
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Elliptical basis functions for segment modeling
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
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
Word graphs: an efficient interface between continuous-speech recognition and language understanding
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Exploiting variable-width features in large vocabulary speech recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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The authors introduce a new, more efficient algorithm, the word-dependent N-best algorithm, for finding multiple sentence hypotheses. The proposed algorithm is based on the assumption that the beginning time of a word depends only on the preceding word. The authors compare this algorithm with two other algorithms for finding the N-best hypotheses: the exact sentence-dependent method and a computationally efficient lattice N-best method. Although the word-dependent algorithm is computationally much less expensive than the exact algorithm, it appears to result in the same accuracy. The lattice method, which is still more efficient, has a significantly higher error rate. It is demonstrated that algorithms that use Viterbi scoring have significantly higher error rates than those that use total likelihood scoring.