Sequentially finding the N-best list in hidden Markov models

Dennis Nilsson, Jacob Goldberger

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)1280-1285
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2001
Externally publishedYes
Event17th International Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA, United States
Duration: 4 Aug 200110 Aug 2001


Dive into the research topics of 'Sequentially finding the N-best list in hidden Markov models'. Together they form a unique fingerprint.

Cite this