A Fast Discriminative Training Algorithm For Minimum Classification Error
; Mendes, H.
; Veiga, A.
A Fast Discriminative Training Algorithm For Minimum Classification Error, Proc I Iberian SLTech - I Joint SIG-IL/Microsoft Workshop on Speech and Language Technologies for Iberian Languages, Lisbon, Poland, Vol. 1, pp. 53 - 56, September, 2009.
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In this paper a new algorithm is proposed for fast discriminative training of hidden Markov models (HMMs) based on minimum classification error (MCE). The algorithm is able to train acoustic models in a few iterations, thus overcoming the slow training speed typical of discriminative training methods based on gradient-descendent. The algorithm tries to cancel the gradient of the objective function in every iteration. Re-estimation expressions of the HMM parameters are derived. Experiments with triphone and word models show that the proposed algorithm always achieves much better results in a single iteration than MCE, MMI or MPE do over several iterations.