Margin Based Learning: A Framework for Acoustic Model Parameter Estimation

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Syed Abbas Ali 1,* Najmi Ghani Haider 2 Mahmood K. Pathan 2

1. Computer & Information Systems Engineering, N.E.D University of Engineering & Technology

2. Computer Science & Information Technology, N.E.D University of Engineering & Technology

* Corresponding author.


Received: 3 Feb. 2012 / Revised: 11 Jun. 2012 / Accepted: 15 Sep. 2012 / Published: 8 Nov. 2012

Index Terms

Statistical Learning, Generalization Capability, Empirical Risk, Discriminative Training, Test Risk Bound


Statistical learning theory has been introduced in the field of machine learning since last three decades. In speech recognition application, SLT combines generalization function and empirical risk in single margin based objective function for optimization. This paper incorporated separation (misclassification) measures conforming to conventional discriminative training criterion in loss function definition of margin based method to derive the mathematical framework for acoustic model parameter estimation and discuss some important issues related to hinge loss function of the derived model to enhance the performance of speech recognition system.

Cite This Paper

Syed Abbas Ali, Najmi Ghani Haider, Mahmood Khan Pathan, "Margin Based Learning: A Framework for Acoustic Model Parameter Estimation", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.12, pp.26-31, 2012. DOI:10.5815/ijisa.2012.12.04


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