Large Margin Discriminative Semi-Markov Model for Phonetic Recognition

This paper considers a large margin discriminative semi-Markov model (LMSMM) for phonetic recognition. The hidden Markov model (HMM) framework that is often used for phonetic recognition assumes only local statistical dependencies between adjacent observations, and it is used to predict a label for each observation without explicit phone segmentation. On the other hand, the semi-Markov model (SMM) framework allows simultaneous segmentation and labeling of sequential data based on a segment-based Markovian structure that assumes statistical dependencies among all the observations within a phone segment. For phonetic recognition which is inherently a joint segmentation and labeling problem, the SMM framework has the potential to perform better than the HMM framework at the expense of slight increase in computational complexity. The SMM framework considered in this paper is based on a non-probabilistic discriminant function that is linear in the joint feature map which attempts to capture long-range statistical dependencies among observations. The parameters of the discriminant function are estimated by a large margin learning framework for structured prediction. The parameter estimation problem in hand leads to an optimization problem with many margin constraints, and this constrained optimization problem is solved using a stochastic gradient descent algorithm. The proposed LMSMM outperformed the large margin discriminative HMM in the TIMIT phonetic recognition task.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2011-09
Language
ENG
Keywords

SPEECH RECOGNITION; SEGMENT MODEL; CLASSIFICATION; HMM

Citation

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, v.19, no.7, pp.1999 - 2012

ISSN
1558-7916
URI
http://hdl.handle.net/10203/100196
Appears in Collection
EE-Journal Papers(저널논문)
  • Hit : 210
  • Download : 7
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button
⊙ Cited 3 items in WoSClick to see citing articles inrecords_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0