Sequentially finding the N-Best List in Hidden Markov Models

Nilsson, Dennis; Goldberger, Jacob

Sequentially finding the N-Best List in Hidden Markov Models

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Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 1280-1284.

Abstract

We propose a novel method to obtain the N -best list of hypotheses in hidden Markov model (HMM). We show that the entire information needed to compute the N -best list from the HMM trellis graph is encapsulated in entities that can be computed in a single forward-backward iteration that usually yields the most likely state sequence. The hypotheses list can then be extracted in a sequential manner from these entities without the need to refer back to the original data of the HMM. Furthermore, our approach can yield significant savings of computational time when compared to traditional methods.

Dennis Nilsson, and Jacob Goldberger. 2001. Sequentially finding the N-Best List in Hidden Markov Models. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01. IJCAI, 1280–1284. https://ijcai.org/proceedings/2001/168

@inproceedings{ijcai2001p168,
  title     = {Sequentially finding the N-Best List in Hidden Markov Models},
  author    = {Nilsson, Dennis and Goldberger, Jacob},
  booktitle = {Proceedings of the Seventeenth International Joint Conference on
               Artificial Intelligence, {IJCAI-01}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {1280--1284},
  year      = {2001},
  url       = {https://ijcai.org/proceedings/2001/168},
}

TY  - CPAPER
TI  - Sequentially finding the N-Best List in Hidden Markov Models
AU  - Nilsson, Dennis
AU  - Goldberger, Jacob
T2  - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2001
SP  - 1280
EP  - 1284
UR  - https://ijcai.org/proceedings/2001/168
ER  -