A narrative approach for speech signal based MMSE estimation using quantum parameters

Authors:
S. Sasikumar;S. Karthikeyan;M. Suganthi;M. Madheswaran
Affiliations:
Department of Electronics and Communication Engineering, P.S.N.A. College of Engineering and Technology, Dindigul, India;Department of Electronics and Communication Engineering, P.S.N.A. College of Engineering and Technology, Dindigul, India;Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, India;Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, India
Venue:
WSEAS Transactions on Signal Processing
Year:
2007

Citing 6
Cited 1

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Orthogonal and projected orthogonal matched filter detection

Signal Processing
MMSE channel estimation scheme based on two-dimensional hadamard transform for OFDM systems

MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Minimax MSE estimation of deterministic parameters with noise covariance uncertainties

IEEE Transactions on Signal Processing
Covariance shaping least-squares estimation

IEEE Transactions on Signal Processing
MMSE whitening and subspace whitening

IEEE Transactions on Information Theory

Robust Romanian language automatic speech recognizer based on multistyle training

WSEAS Transactions on Computer Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the performance of different estimators in estimating the speech signal through Quantum parameters can be analyzed. The main objective is to estimate the speech signal by a set of linear and Non-linear estimators that are proposed to be efficient in performance. The Minimax mean square error estimator is designed to minimize the worst-case MSE. In an estimation context, the objective typically is to minimize the size of the estimation error, rather than that of the data error as a cause, in many practical scenarios the least-squares estimator is known to result in a large MSE. A comparative analysis between MMSE estimator with other linear and nonlinear estimators can be performed. The analysis proved that the MMSE estimator can outperform both from linear and nonlinear estimator.