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» Feature versus model based noise robustness
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ICASSP
2011
IEEE
12 years 11 months ago
Non-stationary feature extraction for automatic speech recognition
In current speech recognition systems mainly Short-Time Fourier Transform based features like MFCC are applied. Dropping the short-time stationarity assumption of the voiced speec...
Zoltán Tüske, Pavel Golik, Ralf Schl&u...
3DPVT
2004
IEEE
316views Visualization» more  3DPVT 2004»
13 years 11 months ago
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
Abstract-General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from spa...
Volker Blanz, Albert Mehl, Thomas Vetter, Hans-Pet...
ICASSP
2008
IEEE
14 years 2 months ago
Towards the use of full covariance models for missing data speaker recognition
This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assumption ...
Marco Kühne, Daniel Pullella, Roberto Togneri...
TASLP
2008
133views more  TASLP 2008»
13 years 6 months ago
Minimum Mean-Squared Error Estimation of Mel-Frequency Cepstral Coefficients Using a Novel Distortion Model
In this paper, a new method for statistical estimation of Mel-frequency cepstral coefficients (MFCCs) in noisy speech signals is proposed. Previous research has shown that model-ba...
Kevin M. Indrebo, Richard J. Povinelli, Michael T....
ECCV
2006
Springer
14 years 9 months ago
EMD-L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors
We propose a fast algorithm, EMD-L1, for computing the Earth Mover's Distance (EMD) between a pair of histograms. Compared to the original formulation, EMD-L1 has a largely si...
Haibin Ling, Kazunori Okada