Notice of Violation of IEEE Publication Principles - A new approach for robust speech recognition using minimum variance distortionless response

Abstract

Notice of Violation of IEEE Publication Principles A Approach for Robust Speech using Minimum Variance Distortionless Response by V. Srinas, Ch. Santhi Rani in the Proceedings of the 2nd International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), March 2015 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates the original text and figures from the paper cited below. The original text was copied with minor edits, without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition by Sanaz Seyedin, Seyed Mohammad Ahadi, Saeed Gazor in The Scientific World Journal, Volume 2013 Hindawi In this paper, we proposed a new technique presents an extraction method for robust speech recognition using the MVDR (Minimum Variance Distortionless Response) spectrum of short time autocorrelation sequence which can reduce the effects of leftover of the nonstationary additive noise that remains after filtering the autocorrelation. To produce a further robust front-end, we present the customized robust distortionless constraint of the MVDR spectral estimation method through revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjust it to the new proposed technique. The new proposed functions allow the components of the input signal at the frequencies with minimum affected by noise to pass with better weights and attenuate more effectively the noisy and unwanted components. This revision results in decrease of the noise residuals of the projected spectrum from the filtered autocorrelation sequence, thus advancing to a more robust algorithm. Our proposed technique, when analyzed on Aurora 2 task for recognition applications, best performed all MFCC (Mel frequency cepstral coefficients) as the fundamental, respective autocorrelation sequence MFCC (RAS MFCC), and the proposed MVDR related features in numerous different noisy conditions.