Probability
Fundamentals of speech recognition
Fundamentals of speech recognition
Multirate systems and filter banks
Multirate systems and filter banks
Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Construction and Evaluation of a Large In-Car Speech Corpus
IEICE - Transactions on Information and Systems
Speaker identification in mismatch training and testing conditions
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Environmental Independent ASR Model Adaptation/Compensation by Bayesian Parametric Representation
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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We present Jump Function Kolmogorov (JFK), a novel signal representation, which is a) additive, thus the sum of signal and noise yields the sum of their JFKs; b) sparse, therefore the signal and noise are separable in this domain. In this paper, the proposed signal representation is used in developing a classification system under noise-mismatch conditions. In this framework, we estimate JFKs from noisy signals in wavelet domain and compare them with the templates trained in clean condition. As the JFK is additive and sparse, the noise is simply eliminated by limiting JFKs only within the confidence intervals. The experiments show that the JFK-driven method significantly outperforms the conventional ones in three different classification tasks. The proposed method is further improved by adopting a discriminative feature selection for the classification.