Speech recognition in noisy environments: a survey
Speech Communication
Cepstral domain segmental feature vector normalization for noise robust speech recognition
Speech Communication - Special issue on robust speech recognition
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Speech recognition in noisy environments
Speech recognition in noisy environments
Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition
Image Processing - Principles and Applications
Image Processing - Principles and Applications
Higher order cepstral moment normalization for improved robust speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
A Discriminative and Heteroscedastic Linear Feature Transformation for Multiclass Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Cepstral Vector Normalization Based on Stereo Data for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
MVA Processing of Speech Features
IEEE Transactions on Audio, Speech, and Language Processing
Quantile based histogram equalization for noise robust large vocabulary speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Probabilistic modulation spectrum factorization for robust speech recognition
ROCLING '11 ROCLING 2011 Poster Papers
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Histogram equalization (HEQ) is one of the most efficient and effective techniques that have been used to reduce the mismatch between training and test acoustic conditions. However, most of the current HEQ methods are merely performed in a dimension-wise manner and without allowing for the contextual relationships between consecutive speech frames. In this paper, we present several novel HEQ approaches that exploit spatial-temporal feature distribution characteristics for speech feature normalization. The automatic speech recognition (ASR) experiments were carried out on the Aurora-2 standard noise-robust ASR task. The performance of the presented approaches was thoroughly tested and verified by comparisons with the other popular HEQ methods. The experimental results show that for clean-condition training, our approaches yield a significant word error rate reduction over the baseline system, and also give competitive performance relative to the other HEQ methods compared in this paper.