A comparative study of noise estimation algorithms for VTS-based robust speech recognition
Yong Hui Zhao, Biing-Hwang Fred Juang · 2010
We conduct a comparative study to investigate two noise es-timation approaches for robust speech recognition using vec-tor Taylor series (VTS) developed in the past few years. The first approach, iterative root finding (IRF), directly differenti-ates the EM auxiliary function and approximates the root of the derivative function through recursive refinements. The second approach, twofold expectation maximization (TEM), estimates noise distributions by regarding them as hidden variables in a modified EM fashion. Mathematical derivations reveal the sub-stantial connection between the two approaches. Two experi-ments are performed in evaluating the performance and conver-gence rate of the algorithms. The first is to fit a GMM model to artificially corrupted samples that are generated through Monte Carlo simulation. The second is to perform speech recognition on the Aurora 2 database. Index Terms: Robust speech recognition, vector Taylor series, noise estimation