Method of moments learning for left-to-right Hidden Markov models

Y. Cem Subakan, Johannes Traa, Paris Smaragdis, Daniel Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a method-of-moments algorithm for parameter learning in Left-to-Right Hidden Markov Models. Compared to the conventional Expectation Maximization approach, the proposed algorithm is computationally more efficient, and hence more appropriate for large datasets. It is also asymptotically guaranteed to estimate the correct parameters. We show the validity of our approach with a synthetic data experiment and a word utterance onset detection experiment.

Original languageEnglish (US)
Title of host publication2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479974504
DOIs
StatePublished - Nov 24 2015
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015 - New Paltz, United States
Duration: Oct 18 2015Oct 21 2015

Publication series

Name2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015

Other

OtherIEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2015
Country/TerritoryUnited States
CityNew Paltz
Period10/18/1510/21/15

Keywords

  • Left-to-Right Hidden Markov Models
  • Method of Moments

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Media Technology

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