Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parallel model combination (DPMC), and unscented transform (UT).  demonstrate that the commonly used approaches that iteratively approximate the noise parameters in an EM framework are variants of the Gauss-Newton method. Through the formulation of the Gauss-Newton method for estimating noise means and variances, the noise estimation problems are reduced to determining the Jacobians of the noisy speech distributions. For the sampling-based compensations,  present two methods, sample Jacobian average (SJA) and cross-covariance (XCOV), to evaluate the Jacobians. Experiments on the Aurora 2 database verify the efficacy of the Gauss-Newton method to these noise compensation models.
['Yong Zhao', 'Biing-Hwang Juang']
I am writing a research paper on a idea Non-linear noise compensation for robust speech recognition using Gauss-Newton method
[{"from": "human", "value": "I am writing a research paper on a idea Non-linear noise compensation for robust speech recognition using Gauss-Newton method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parallel model combination (DPMC), and unscented transform (UT).  demonstrate that the commonly used approaches that iteratively approximate the noise parameters in an EM framework are variants of the Gauss-Newton method. Through the formulation of the Gauss-Newton method for estimating noise means and variances, the noise estimation problems are reduced to determining the Jacobians of the noisy speech distributions. For the sampling-based compensations,  present two methods, sample Jacobian average (SJA) and cross-covariance (XCOV), to evaluate the Jacobians. Experiments on the Aurora 2 database verify the efficacy of the Gauss-Newton method to these noise compensation models."}]