title: New features using robust MVDR spectrum of filtered autocorrelation sequence for robust speech recognition.
abstract: This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions.
authors: Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada K7L 3N6.
authors: Sanaz
authors: S
authors: Seyedin
authors: Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15914, Iran.
authors: Seyed Mohammad
authors: SM
authors: Ahadi
authors: Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada K7L 3N6.
authors: Saeed
authors: S
authors: Gazor
mesh_terms: D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D013018:Sound Spectrography / Q000379:methods*; D013068:Speech Production Measurement / Q000379:methods*; D049250:Speech Recognition Software; D013223:Statistics as Topic