{
    "paper_id": "O12-1001",
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        "date_generated": "2023-01-19T08:02:47.533408Z"
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    "title": "Improved Histogram Equalization Methods for Robust Speech Recognition",
    "authors": [
        {
            "first": "Hsin-Ju",
            "middle": [],
            "last": "Hsieh",
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        {
            "first": "Jeih-Weih",
            "middle": [],
            "last": "Hung",
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        {
            "first": "Berlin",
            "middle": [],
            "last": "Chen",
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            {
                "text": "hsinju@ntnu.edu.tw, berlin@ntnu.edu.tw, jwhung@ncnu.edu.tw (Histogram Equalization, HEQ) [1] ( 1.72% (Advanced Front-End Standard, AFE) [7] 87.17%",
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                "text": "Spatial-Temporal Contextual Statistics) (Differencing) (Averaging) : ( , ) ( 1, ) , 2 ( , ) 2 ( , ), 1 ( , ) ( 1, ) , 2 ( , ) 2 0, 1 s diff s avg x d t x d t d D x d t x d t d x d t x d t d D x d t d ( diff ( diff ( avg ( avg ( 1, ) 2 d ( 1, ( 1, d ( ) x( ) d D ( , ) x( , ) ( , ) 1 0, 0 d 1 ( , ) s diff x d t ( diff ( diff ( , ) s avg x d t ( avg ( avg ( , ) t diff x d t ( diff ( diff ( , ) t avg x d t ( avg ( avg ( , ) diff x d t ( , )",
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            {
                "text": "Keywords: automatic speech recognition, noise robustness, histogram equalization, feature contextual statistics.",
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                "section": "88.22% 8%",
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                "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
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