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There is a growing attention in subspace learning using tensor-based approaches in high dimensional spaces. In this paper we first indicate that these methods suffer from the Heteroscedastic problem and then propose a new approach called Heteroscedastic Multilinear Discriminant Analysis (HMDA). Our method can solve this problem by utilizing the pairwise chernoff distance between every pair of clusters...
This paper has presented a novel discriminative parameters calibration approach based on the model distance maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihood-maximizing framework. In the previous work, for adjusting the spectral over-subtraction factor of SS, conventional ML approach is used that only utilizes the true...
In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is proposed which is an extension to the original 2DPCA. We state that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the covariance matrix of PCA. This implies that 2DPCA eliminates some covariance information that can be useful for recognition. E2DPCA instead of just using the main diagonal...
Channel distortion may dramatically degrade speech recognition performance in a distant environment. Authors in their recent work proposed a novel spectral subtraction method which they named it maximum likelihood based spectral subtraction (MLBSS). They indicated that recognition performance could be improved dramatically by adjusting filter parameters based on recognition results. Previous results...
Face recognition has been an important topic in computer vision for the last two decades. While many algorithms have been developed to address this issue, one of the major challenges faced by them is variation in pose. One of the possible solutions is to find invariant features among different poses of a single person. In this paper a geometry mapping between a frontal face and its rotated pose is...
In this paper we present a novel approach for adjusting a multi band spectral subtraction filter coefficients based on speech recognition system results. Currently most speech enhancement techniques are designed according to various waveform level criteria such as maximizing SNR or minimizing signal error. However improvement in these criteria does not necessarily result in increasing speech recognition...
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