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Robust automatic speech recognition with missing and unreliable acoustic data
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Prospect features and their application to missing data techniques for robust speech recognition
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Jeju Island, Korea, October
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H. Van hamme, "Prospect features and their application to missing data techniques for robust speech recognition," in Proc. ICSLP, Jeju Island, Korea, October 2004, pp. 101-104.
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Improved noise-robustness in distributed speech recognition via perceptually-weighted vector quantisation of filterbank energies
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Lisbon, Portugal, Sept
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Toulouse, France
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Robust speech recognition using cepstral domain missing data techniques and noisy masks
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Montreal, Canada, May
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H. Van hamme, "Robust speech recognition using cepstral domain missing data techniques and noisy masks," in Proc. ICASSP, Montreal, Canada, May 2004, pp. 213-216.
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Classifier-based mask estimation for missing feature methods of robust speech recognition
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Beijing, China
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Speech/non-speech discrimination based on contextual information integrated bispectrum lrt
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0035396555
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Noise power spectral density estimation based on optimal smoothing and minimum statistics
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July
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Martin, R.1
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Handling convolutional noise in missing data automatic speech recognition
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Pittsburgh, U.S.A, Sept
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M. Van Segbroeck and H. Van hamme, "Handling convolutional noise in missing data automatic speech recognition," in Proc. ICSLP, Pittsburgh, U.S.A., Sept. 2006, pp. 2526-2565.
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ESAT-PSI Speech Group website, http://www.esat.kuleuven.be/psi/spraak/.
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Group website
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