Benjamin Aw
Add updated pkl file v3
6fa4bc9
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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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"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>",
"type_str": "table",
"html": null,
"text": "69308027@cc.ntnu.edu.tw, ymyeh@ice.ntnu.edu.tw, berlin@csie.ntnu.edu.tw",
"num": null
},
"TABREF1": {
"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>",
"type_str": "table",
"html": null,
"text": "",
"num": null
}
}
}
}