Title
Regularized minimum variance distortionless response-based cepstral features for robust continuous speech recognition
Abstract
•We study the low-variance and robust features for speech recognition system on the AURORA-4 corpus.•We propose to compute cepstral features from a regularized MVDR (RMVDR) spectral estimates, denoted as RMVDR-based Cepstral Coefficient (RMCC) features.•A sigmoid-shape auditory domain weighting rule is proposed for speech spectrum enhancement and incorporated in to the RMCC framework.•We incorporate the medium duration power bias subtraction (MDPBS) method in to the RMCC framework.•Two robust front-ends are proposed, robust RMCC (RRMCC) and Normalized RMCC (NRMCC) for speech recognition.
Year
DOI
Venue
2015
10.1016/j.specom.2015.07.007
Speech Communication
Keywords
Field
DocType
Speech recognition,Robust feature extraction,Regularized MVDR,ASE,Feature normalization,Multi-condition training
Mel-frequency cepstrum,Minimum-variance unbiased estimator,Pattern recognition,Computer science,Cepstrum,Word error rate,Feature extraction,Robustness (computer science),Speech recognition,Spectral density,Artificial intelligence,Estimator
Journal
Volume
Issue
ISSN
73
C
0167-6393
Citations 
PageRank 
References 
2
0.44
30
Authors
3
Name
Order
Citations
PageRank
jahangir alam132038.69
Patrick Kenny22700214.80
Douglas D. O'Shaughnessy339884.79