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"text": "(Non-Casual Moving Average) 8 ~, 1 2~ otherwise y L T t L if L y y t L L i i t t (12) (Casual Moving Average) ~, 1 0 otherwise y T t L if L y y t L i i t t (13) (Non-Casual Auto Regression Moving Average) ~, 1 21 0 otherwise y L T t L if L y y y t L i L j j t i t t (14) (Casual Auto Regression Moving Average) ~, 1 21 0 otherwise y T t L if L y y y t L i L j j t i t t (15) i \u1ef9 i \u0177 L", |
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"section": "(Polynomial-Fit Histogram Equalization, PHEQ)", |
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"section": "(Polynomial-Fit Histogram Equalization, PHEQ)", |
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"section": "(Polynomial-Fit Histogram Equalization, PHEQ)", |
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"raw_str": ") (i noisy y i (16) ) (i clean y (16)", |
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|
"BIBREF0": { |
|
"ref_id": "b0", |
|
"title": "Speech Recognition in noisy environments: A survey", |
|
"authors": [ |
|
{ |
|
"first": "Y", |
|
"middle": [], |
|
"last": "Gong", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1995, |
|
"venue": "Speech communication", |
|
"volume": "16", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Y. Gong, \"Speech Recognition in noisy environments: A survey,\" Speech communication, Vol.16, 1995.", |
|
"links": null |
|
}, |
|
"BIBREF1": { |
|
"ref_id": "b1", |
|
"title": "Supperssion of Acoutstic Noise in Speech Using Spectral Subtraction", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [ |
|
"F" |
|
], |
|
"last": "Boll", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1979, |
|
"venue": "IEEE Trans. on ASSP", |
|
"volume": "27", |
|
"issue": "2", |
|
"pages": "133--120", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S.F. Boll, \"Supperssion of Acoutstic Noise in Speech Using Spectral Subtraction,\" IEEE Trans. on ASSP, Vol.27, No.2, pp.133-120, 1979.", |
|
"links": null |
|
}, |
|
"BIBREF2": { |
|
"ref_id": "b2", |
|
"title": "Spoken Language Processing: A Guide to Theory, Algorithm and System Development", |
|
"authors": [ |
|
{ |
|
"first": "X", |
|
"middle": [], |
|
"last": "Huang", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "A", |
|
"middle": [], |
|
"last": "Acero", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "H", |
|
"middle": [], |
|
"last": "Hon", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2001, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "X. Huang, A. Acero and H. Hon, \"Spoken Language Processing: A Guide to Theory, Algorithm and System Development,\" Prentice Hall PTR Upper Saddle River, NJ, USA, 2001.", |
|
"links": null |
|
}, |
|
"BIBREF3": { |
|
"ref_id": "b3", |
|
"title": "Cepstral Analysis Techniques for Automatic Speaker Verification", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Furui", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1981, |
|
"venue": "IEEE Trans. on ASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Furui, \"Cepstral Analysis Techniques for Automatic Speaker Verification,\" IEEE Trans. on ASSP, 1981.", |
|
"links": null |
|
}, |
|
"BIBREF4": { |
|
"ref_id": "b4", |
|
"title": "Cepstral Domain Segmental Feature Vector Normalization for Noise Robust Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "A", |
|
"middle": [], |
|
"last": "Viikki", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "K", |
|
"middle": [], |
|
"last": "Laurila", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1998, |
|
"venue": "Speech Communication", |
|
"volume": "25", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "A. Viikki and K. Laurila, \"Cepstral Domain Segmental Feature Vector Normalization for Noise Robust Speech Recognition,\" Speech Communication, Vol. 25, 1998.", |
|
"links": null |
|
}, |
|
"BIBREF5": { |
|
"ref_id": "b5", |
|
"title": "Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains", |
|
"authors": [ |
|
{ |
|
"first": "J", |
|
"middle": [ |
|
"L" |
|
], |
|
"last": "Gauian", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"H" |
|
], |
|
"last": "Lee", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1994, |
|
"venue": "IEEE Trans. on Speech and Audio Processing", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "J.L. Gauian and C.H. Lee, \"Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains,\" IEEE Trans. on Speech and Audio Processing, 1994.", |
|
"links": null |
|
}, |
|
"BIBREF6": { |
|
"ref_id": "b6", |
|
"title": "Maximum Likelihood Linear Regression for Speaker Adaptation of Continuous Density Hidden Markov Models", |
|
"authors": [ |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"J" |
|
], |
|
"last": "Leggetter", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "P", |
|
"middle": [ |
|
"C" |
|
], |
|
"last": "Woodland", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1995, |
|
"venue": "Computer Speech and Language", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "C.J. Leggetter and P.C. Woodland, \"Maximum Likelihood Linear Regression for Speaker Adaptation of Continuous Density Hidden Markov Models,\" Computer Speech and Language, 1995.", |
|
"links": null |
|
}, |
|
"BIBREF7": { |
|
"ref_id": "b7", |
|
"title": "Cepstrum Third-order Normalization Method for Noisy Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "Y", |
|
"middle": [ |
|
"H" |
|
], |
|
"last": "Suk", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "S", |
|
"middle": [ |
|
"H" |
|
], |
|
"last": "Choi", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "H", |
|
"middle": [ |
|
"S" |
|
], |
|
"last": "Lee", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 1999, |
|
"venue": "Electronics Letters", |
|
"volume": "35", |
|
"issue": "7", |
|
"pages": "527--528", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "Y. H. Suk, S. H. Choi, H. S. Lee, \"Cepstrum Third-order Normalization Method for Noisy Speech Recognition,\" Electronics Letters, Vol. 35, no. 7, pp. 527-528, April 1999.", |
|
"links": null |
|
}, |
|
"BIBREF8": { |
|
"ref_id": "b8", |
|
"title": "Higher Order Cepstral Moment Normalization (HOCMN) for Robust Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"W" |
|
], |
|
"last": "Hsu", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "L", |
|
"middle": [ |
|
"S" |
|
], |
|
"last": "Lee", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2004, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "C.W. Hsu and L.S. Lee, \"Higher Order Cepstral Moment Normalization (HOCMN) for Robust Speech Recognition,\" in Proc. ICASSP 2004.", |
|
"links": null |
|
}, |
|
"BIBREF9": { |
|
"ref_id": "b9", |
|
"title": "A Nonlinear Unsupervised Adaptation Technique for Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Dharanipargda", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "M", |
|
"middle": [], |
|
"last": "Padmanabhan", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2000, |
|
"venue": "Proc. ICSLP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Dharanipargda and M. Padmanabhan, \"A Nonlinear Unsupervised Adaptation Technique for Speech Recognition,\" in Proc. ICSLP 2000.", |
|
"links": null |
|
}, |
|
"BIBREF10": { |
|
"ref_id": "b10", |
|
"title": "Joint Uncertainty Decoding (JUD) with Histogram-Based Quantization (HQ) for Robust and/or Distributed Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"Y" |
|
], |
|
"last": "Wan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "L", |
|
"middle": [ |
|
"S" |
|
], |
|
"last": "Lee", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2006, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "C. Y. Wan and L.S. Lee, \"Joint Uncertainty Decoding (JUD) with Histogram-Based Quantization (HQ) for Robust and/or Distributed Speech Recognition,\" in Proc. ICASSP 2006.", |
|
"links": null |
|
}, |
|
"BIBREF11": { |
|
"ref_id": "b11", |
|
"title": "Histogram-based quantization (HQ) for robust and scalable distributed speech recognition", |
|
"authors": [ |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"Y" |
|
], |
|
"last": "Wan", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "L", |
|
"middle": [ |
|
"S" |
|
], |
|
"last": "Lee", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2005, |
|
"venue": "Proc. EUROSPEECH", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "C.Y. Wan and L.S. Lee, \"Histogram-based quantization (HQ) for robust and scalable distributed speech recognition,\" in Proc. EUROSPEECH 2005.", |
|
"links": null |
|
}, |
|
"BIBREF12": { |
|
"ref_id": "b12", |
|
"title": "Quantile based histogram equalization for noise robust speech recognition", |
|
"authors": [ |
|
{ |
|
"first": "F", |
|
"middle": [], |
|
"last": "Hilger", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "H", |
|
"middle": [], |
|
"last": "Ney", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2001, |
|
"venue": "Proc. EUROPSEECH", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "F. Hilger, H. Ney, \"Quantile based histogram equalization for noise robust speech recognition,\" in Proc. EUROPSEECH 2001.", |
|
"links": null |
|
}, |
|
"BIBREF13": { |
|
"ref_id": "b13", |
|
"title": "Quantile Based Histogram Equalization for Noise Robust Large Vocabulary Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "F", |
|
"middle": [], |
|
"last": "Hilger", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2005, |
|
"venue": "IEEE Trans. on Speech and Audio Processing", |
|
"volume": "14", |
|
"issue": "3", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "F. Hilger et al., \"Quantile Based Histogram Equalization for Noise Robust Large Vocabulary Speech Recognition,\" IEEE Trans. on Speech and Audio Processing, Vol. 14(3), 2005.", |
|
"links": null |
|
}, |
|
"BIBREF14": { |
|
"ref_id": "b14", |
|
"title": "Non-linear Transformation of the Feature Space for Robust Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "A", |
|
"middle": [], |
|
"last": "De La", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Torre", |
|
"middle": [], |
|
"last": "", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2002, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "A. de la Torre et al., \"Non-linear Transformation of the Feature Space for Robust Speech Recognition,\" in Proc. ICASSP 2002.", |
|
"links": null |
|
}, |
|
"BIBREF15": { |
|
"ref_id": "b15", |
|
"title": "Histogram Based Normalization in the Acoustic Feature Space", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Molau", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2001, |
|
"venue": "Proc. ASRU", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Molau et al., \"Histogram Based Normalization in the Acoustic Feature Space,\" in Proc. ASRU 2001.", |
|
"links": null |
|
}, |
|
"BIBREF16": { |
|
"ref_id": "b16", |
|
"title": "Feature Space Normalization in Adverse Acoustic Conditions", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Molau", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2003, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Molau et al., \"Feature Space Normalization in Adverse Acoustic Conditions,\" in Proc. ICASSP 2003.", |
|
"links": null |
|
}, |
|
"BIBREF17": { |
|
"ref_id": "b17", |
|
"title": "Histogram Normalization in the Acoustic Feature Space", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Molau", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2002, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Molau et al., \"Histogram Normalization in the Acoustic Feature Space,\" in Proc. ICASSP 2002.", |
|
"links": null |
|
}, |
|
"BIBREF18": { |
|
"ref_id": "b18", |
|
"title": "Cepstral domain segmental nonlinear feature transformations for robust speech recognition", |
|
"authors": [ |
|
{ |
|
"first": "J", |
|
"middle": [ |
|
"C" |
|
], |
|
"last": "Segura", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2004, |
|
"venue": "IEEE Signal Processing Letters", |
|
"volume": "11", |
|
"issue": "5", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "J. C. Segura et al., \"Cepstral domain segmental nonlinear feature transformations for robust speech recognition,\" IEEE Signal Processing Letters, Vol. 11(5), 2004.", |
|
"links": null |
|
}, |
|
"BIBREF19": { |
|
"ref_id": "b19", |
|
"title": "Histogram equalization of the speech representation for robust speech recognition", |
|
"authors": [ |
|
{ |
|
"first": "A", |
|
"middle": [], |
|
"last": "De La", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Torre", |
|
"middle": [], |
|
"last": "", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2005, |
|
"venue": "IEEE Trans. on Speech and Audio Processing", |
|
"volume": "13", |
|
"issue": "3", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "A. de la Torre et al., \"Histogram equalization of the speech representation for robust speech recognition,\" IEEE Trans. on Speech and Audio Processing, Vol. 13(3), 2005.", |
|
"links": null |
|
}, |
|
"BIBREF20": { |
|
"ref_id": "b20", |
|
"title": "Exploiting Polynomial-Fit Histogram Equalization and Temporal Average for Robust Speech Recognition", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [ |
|
"H" |
|
], |
|
"last": "Lin", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "Y", |
|
"middle": [ |
|
"M" |
|
], |
|
"last": "Yeh", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "B", |
|
"middle": [], |
|
"last": "Chen", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2006, |
|
"venue": "Proc. ICSLP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S.H. Lin, Y.M. Yeh and B. Chen, \"Exploiting Polynomial-Fit Histogram Equalization and Temporal Average for Robust Speech Recognition,\" in Proc. ICSLP 2006.", |
|
"links": null |
|
}, |
|
"BIBREF21": { |
|
"ref_id": "b21", |
|
"title": "Low-Resource Noise-Robust Feature Post-Processing on Aurora 2.0", |
|
"authors": [ |
|
{ |
|
"first": "C", |
|
"middle": [ |
|
"P" |
|
], |
|
"last": "Chen", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "J", |
|
"middle": [], |
|
"last": "Bilmes", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "K", |
|
"middle": [], |
|
"last": "Kirchhoff", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2002, |
|
"venue": "Proc. ICSLP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "C.P. Chen, J. Bilmes and K. Kirchhoff, \"Low-Resource Noise-Robust Feature Post-Processing on Aurora 2.0,\" in Proc. ICSLP 2002.", |
|
"links": null |
|
}, |
|
"BIBREF22": { |
|
"ref_id": "b22", |
|
"title": "The AURORA Experimental Framework for the Performance Evaluations of Speech Recognition Systems under Noisy Conditions", |
|
"authors": [ |
|
{ |
|
"first": "H", |
|
"middle": [ |
|
"G" |
|
], |
|
"last": "Hirsch", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "D", |
|
"middle": [], |
|
"last": "Pearce", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2000, |
|
"venue": "Proc. ISCA ITRW ASR", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "H. G. Hirsch, D. Pearce, \"The AURORA Experimental Framework for the Performance Evaluations of Speech Recognition Systems under Noisy Conditions,\" in Proc. ISCA ITRW ASR 2000.", |
|
"links": null |
|
}, |
|
"BIBREF23": { |
|
"ref_id": "b23", |
|
"title": "The HTK Book Version 3.3", |
|
"authors": [ |
|
{ |
|
"first": "S", |
|
"middle": [], |
|
"last": "Young", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2005, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "S. Young et al., \"The HTK Book Version 3.3,\" 2005.", |
|
"links": null |
|
}, |
|
"BIBREF24": { |
|
"ref_id": "b24", |
|
"title": "Large-Vocabulary Speech Recognition under Adverse Acoustic Environments", |
|
"authors": [ |
|
{ |
|
"first": "L", |
|
"middle": [], |
|
"last": "Deng", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "A", |
|
"middle": [], |
|
"last": "Acero", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "M", |
|
"middle": [], |
|
"last": "Plumpe", |
|
"suffix": "" |
|
}, |
|
{ |
|
"first": "X", |
|
"middle": [], |
|
"last": "Huang", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2000, |
|
"venue": "Proc. ICSLP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "L. Deng, A. Acero, M. Plumpe and X. Huang. \"Large-Vocabulary Speech Recognition under Adverse Acoustic Environments,\" in Proc. ICSLP 2000.", |
|
"links": null |
|
}, |
|
"BIBREF25": { |
|
"ref_id": "b25", |
|
"title": "Maximum Likelihood Multiple Projection Schemes for Hidden Markov Models", |
|
"authors": [ |
|
{ |
|
"first": "M", |
|
"middle": [ |
|
"J F" |
|
], |
|
"last": "Gales", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2001, |
|
"venue": "", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "M. J. F. Gales, \"Maximum Likelihood Multiple Projection Schemes for Hidden Markov Models,\" Cambridge University Technical Report RT-365, 2001.", |
|
"links": null |
|
}, |
|
"BIBREF26": { |
|
"ref_id": "b26", |
|
"title": "Maximum Likelihood Discriminant Feature Spaces", |
|
"authors": [ |
|
{ |
|
"first": "G", |
|
"middle": [], |
|
"last": "Saon", |
|
"suffix": "" |
|
} |
|
], |
|
"year": 2000, |
|
"venue": "Proc. ICASSP", |
|
"volume": "", |
|
"issue": "", |
|
"pages": "", |
|
"other_ids": {}, |
|
"num": null, |
|
"urls": [], |
|
"raw_text": "G. Saon et al., \"Maximum Likelihood Discriminant Feature Spaces,\" in Proc. ICASSP 2000. [28] , \" ,\" , 2005.", |
|
"links": null |
|
} |
|
}, |
|
"ref_entries": { |
|
"TABREF0": { |
|
"content": "<table><tr><td/><td/><td/><td/><td/><td/><td colspan=\"2\">s</td><td>t</td><td/><td/><td colspan=\"2\">h</td><td>t</td><td>n</td><td>t</td><td>y</td><td>t</td><td>s</td><td>t</td><td>h</td><td>t</td><td>n</td><td>t</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>(</td><td>)</td><td>(</td><td>)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>(Speech Robustness) [11][12]</td></tr><tr><td/><td/><td colspan=\"12\">(Vector Quantization)</td><td>(Distributed Speech Recognition, DSR)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"4\">[10][15]</td><td>(Histogram Equalization)</td><td>[1] (THEQ)</td></tr><tr><td/><td colspan=\"4\">(1) (QHEQ)</td><td/><td/><td/><td/><td/><td colspan=\"5\">(Speech Enhancement) (Quantization Distortion)</td></tr><tr><td>2.1</td><td/><td/><td/><td/><td/><td/><td/><td colspan=\"7\">(Histogram Equalization, HEQ)</td></tr><tr><td/><td/><td colspan=\"13\">(Data Fitting) (Uncorrelated) (Table Look-Up based Histogram Equalization, THEQ)[10] (Inverse Function) (Noisy Speech)</td><td>(Clean</td></tr><tr><td>(</td><td/><td/><td colspan=\"2\">Speech)</td><td/><td colspan=\"2\">)</td><td/><td/><td/><td/><td/><td/><td>(Spectral Subtraction, SS)[2]</td><td>(Wiener (Mel-Frequency</td></tr><tr><td colspan=\"14\">Filter, WF)[3] Cepstral Coefficients, MFCC)</td><td>[16][17][18]</td></tr><tr><td/><td colspan=\"2\">(2)</td><td/><td/><td/><td/><td/><td/><td/><td colspan=\"5\">(Moving Average) (Quantile-based Histogram Equalization, QHEQ)[13,14] (Robust Speech Feature) [10][19][20]</td></tr><tr><td colspan=\"13\">(Transformation Function)</td><td colspan=\"2\">(Non-stationary Noise)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>(Cepstrum Mean Subtraction, CMS)[4] (Grid Search) [19][20]</td><td>x</td><td>(Cepstrum</td></tr><tr><td/><td/><td/><td colspan=\"12\">Mean and Variance Normalization, CMVN)[5]</td><td>(Probability Density Function,</td></tr><tr><td colspan=\"4\">(3) PDF) p Test</td><td>x</td><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"2\">(Acoustic Model Adaptation) (Data Fitting) x F x</td><td>y</td></tr><tr><td/><td colspan=\"9\">AURORA-2 y p Train p Test</td><td>x</td><td/><td/><td/><td>(Adaptation Data) (1)</td></tr><tr><td/><td>p</td><td colspan=\"2\">Train</td><td>y</td><td colspan=\"2\">p</td><td colspan=\"3\">Test</td><td>x</td><td/><td colspan=\"2\">dx dy</td><td>p</td><td>Test</td><td>(Mean Vector) dy F d y F 1 1</td><td>y</td><td>(Covariance Matrix)</td><td>(1)</td></tr><tr><td>1.</td><td colspan=\"2\">F 1</td><td colspan=\"12\">(Maximum a Posteriori, MAP)[6] Regression, MLLR)[7] y x F (Inverse Function)</td><td>(Maximum Likelihood Linear (Cumulative</td></tr><tr><td colspan=\"15\">(Automatic Speech Recognition, ASR) Probability Density Function, CDF) (CMVN) 2. dx x p x x C Test Test</td><td>(CMS)</td></tr><tr><td/><td/><td/><td/><td colspan=\"3\">F</td><td>x</td><td/><td>p</td><td colspan=\"2\">Test</td><td colspan=\"2\">F</td><td>(Moment) dy y dF y</td><td>dy</td><td>(Environment Mismatch) (Background Noise)</td></tr><tr><td/><td/><td/><td/><td colspan=\"3\">y</td><td colspan=\"2\">p</td><td colspan=\"2\">Train</td><td colspan=\"4\">(Channel Effect) dy y x F y</td><td>(Speech Robustness)</td></tr><tr><td/><td/><td/><td/><td colspan=\"2\">C</td><td colspan=\"4\">Train</td><td>y</td><td/><td/><td/><td>[8]</td><td>[9]</td></tr><tr><td colspan=\"14\">(Histogram Equalization)</td><td>[10]</td><td>(Log Energy)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"5\">(Mel Filter-Bank)</td><td>(1)</td><td>(Additive Noise) (2) (Mel-Frequency Cepstral</td></tr><tr><td colspan=\"14\">(Convolutional Noise) Coefficient)</td><td>(Test</td></tr><tr><td colspan=\"8\">(Linearly Additive) Speech)</td><td/><td/><td/><td/><td colspan=\"3\">(Cumulative Density Function, CDF)</td><td>(Training Speech)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"4\">(Reference Distribution)</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>2 3 4</td></tr></table>", |
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"html": null, |
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"text": "69308027@cc.ntnu.edu.tw, ymyeh@ice.ntnu.edu.tw, berlin@csie.ntnu.edu.tw", |
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"content": "<table><tr><td/><td>Lookup)</td><td>(Table Look-Up based</td></tr><tr><td colspan=\"2\">Histogram Equalization, THEQ)</td></tr><tr><td>2.2</td><td colspan=\"2\">(Quantile-based Histogram equalization, QHEQ)</td></tr><tr><td/><td>(Nonparametric)</td></tr><tr><td/><td/><td>[19][20]</td></tr></table>", |
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