A New Family of Extended Baum-Welch Update Rules

Kanevsky, Dr Dimitri and Povey, Dr Daniel and Ramabhadran, Dr Bhuvana and Rish, Dr Irina and Sainath, Dr Tara (2008) A New Family of Extended Baum-Welch Update Rules. [Preprint]

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In this paper, we consider a generalization of the state-of-art discriminative method for optimizing the conditional likelihood in Hidden Markov Models (HMMs), called the Extended Baum-Welch (EBW) algorithm, that has had significant impact on the speech recognition community. We propose a generalized form of EBW update rules that can be associated with a weighted sum of updated and initial models, and demonstrate that using novel update rules can significantly speed up parameter estimation for Gaussian mixtures.

Item Type:Preprint
Keywords:Extended Baum Welch, optimization, speech
Subjects:Computer Science > Speech
ID Code:6037
Deposited By: Kanevsky, Dr Dimitri
Deposited On:30 Apr 2008 18:35
Last Modified:11 Mar 2011 08:57

References in Article

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Cong Liu Peng Liu Hui Jiang Soong, F. Ren-Hua Wang, Constrained Line Search Optimization for Discriminative Training in Speech Recognition, Proc. ICASSP, 2007.

Dimitri Kanevsky, Extended Baum Transformations for General Functions, Proc. ICASSP.

Dimitri Kanevsky, Extended Baum Transformations for General Functions, II, tech. Rep. RC23645(W0506-120), Human Language technologies, IBM.

Daniel Povey, Discriminative Training for Large Vocabulary Speech Recognition, PhD Thesis March 1, Cambridge, UK.

Daniel Povey, Brian Kingsbury, Evaluation of Proposed Modifications to MPE for Large Scale Discriminative Training, ICASSP'07.

Daniel Povey, Dimitri Kanevsky, Brian Kingsbury, Bhuvana Ramabhadran, George Saon and Karthik Visweswariah, Boosted MMI for model and feature-space discriminative training, ICASSP'08.

Tara N. Sainath, Dimitri Kanevsky, Bhuvana Ramabhadran, Gradient Steepness Metrics Using Extended Baum-Welch Transformations for Universal Pattern Recognition Tasks, Proc. ASRU, 2007

P.C. Woodland and D. Povey, Large Scale Discriminative Training of hidden Markov modeles for Speech Recognition, Computer Speech and Language, pp. 25-47, Vol. 16, No. 1, January 2002.


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