Base_JSON/prefixO/json/O12/O12-1023.json

{
    "paper_id": "O12-1023",
    "header": {
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        "date_generated": "2023-01-19T08:03:17.588391Z"
    },
    "title": "Study on Keyword Spotting using Prosodic Attribute Detection for Conversational Speech",
    "authors": [
        {
            "first": "Yu-Jui",
            "middle": [],
            "last": "Huang",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Information Engineering National Chia-Yi University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Yin-Wei",
            "middle": [],
            "last": "Chung",
            "suffix": "",
            "affiliation": {},
            "email": ""
        },
        {
            "first": "Jui-Feng",
            "middle": [],
            "last": "Yeh",
            "suffix": "",
            "affiliation": {},
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "It is one of most essential issues to extract the keywords from conversational speech for understanding the utterances from speakers. This thesis aims at keyword spotting from spontaneous speech for keyword detecting. We proposed prosodic features that are used for keyword detection. The prosody words are segmented from speaker's utterance according to the pre-training decision tree. The supported vector machine is further used as the classifier to judge the prosody word is keyword or not. The prosody word boundary segmentation algorithm based on decision tree is illustrated. Besides the data driven feature, the knowledge obtained from the corpus observation is integrated in the decision tree. Finally, the keyword",
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        "paper_id": "O12-1023",
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        "abstract": [
            {
                "text": "It is one of most essential issues to extract the keywords from conversational speech for understanding the utterances from speakers. This thesis aims at keyword spotting from spontaneous speech for keyword detecting. We proposed prosodic features that are used for keyword detection. The prosody words are segmented from speaker's utterance according to the pre-training decision tree. The supported vector machine is further used as the classifier to judge the prosody word is keyword or not. The prosody word boundary segmentation algorithm based on decision tree is illustrated. Besides the data driven feature, the knowledge obtained from the corpus observation is integrated in the decision tree. Finally, the keyword",
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                "section": "Abstract",
                "sec_num": null
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        ],
        "body_text": [
            {
                "text": "in the focus part are extracted using prosody features by sported vector machine (SVM). According to the experimental results, we can find the proposed method outperform the phone verification approach especially in recall and accuracy. This shows the proposed approach is operative for keyword detecting.",
                "cite_spans": [],
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                "section": "",
                "sec_num": null
            },
            {
                "text": "Keywords: Keyword spotting, prosodic feature, prosody word, spoken language. ",
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                "section": "",
                "sec_num": null
            },
            {
                "text": "( ) i i i P t t D E",
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                "section": "",
                "sec_num": null
            },
            {
                "text": "( 1) ",
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                "sec_num": null
            },
            {
                "text": "EQUATION",
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                        "text": "EQUATION",
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                        "raw_str": "Pi(t) i t i i bi ei i 2 2 ( )( ( ) ) , [ , ] ( ) i i i i e i i t b i i i e t b t t P t P t b e t t E \u00a6 \u00a6 ( 2) t 3 i P i 4 n 1 ( ) 2 i i t e b",
                        "eq_num": "( 3)"
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            {
                "text": "EQUATION",
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                "eq_spans": [
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                        "start": 0,
                        "end": 8,
                        "text": "EQUATION",
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                        "raw_str": "SVM (predict) +1 -1 5 1, 1, i i if T is semantic object T otherwise \u00ae ( 5) SVM (1) 01-10 i n i ij P i j 1 2 { , ,... } i i in i P P P PW Dur ij P i j Bi Ei _ i Syl N i _ ij Syl b i j _ ij Syl e i j",
                        "eq_num": "(2)"
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                "section": "",
                "sec_num": null
            },
            {
                "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
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        "back_matter": [
            {
                "text": "[30] .",
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                "section": "annex",
                "sec_num": null
            },
            {
                "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012)",
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                "ref_spans": [],
                "eq_spans": [],
                "section": "280-312. , , 2008",
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        "ref_entries": {
            "TABREF0": {
                "text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
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                "html": null,
                "type_str": "table",
                "content": "<table><tr><td colspan=\"8\">Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012)</td></tr><tr><td colspan=\"2\">Lee Case 5</td><td colspan=\"2\">Key-Phrase Detection [27] SVM</td><td colspan=\"3\">Verification</td><td>[8] Tatsuya Kawahara</td><td>Chin-Hui SVM [4][5]</td></tr><tr><td colspan=\"2\">Case 6</td><td colspan=\"5\">[9] Fujisaki Model (Keyword Detector) 3</td><td>HPG (Prosodic Word Detection)</td><td>9</td></tr><tr><td colspan=\"4\">(Keyword Detection) Case 7</td><td/><td/><td/></tr><tr><td colspan=\"3\">[4][5][28][29][30]</td><td>HPG</td><td/><td/><td/></tr><tr><td colspan=\"2\">Case 8</td><td colspan=\"2\">&gt; (Pitch reset)</td><td colspan=\"2\">Rose[10]</td><td colspan=\"2\">HMM</td></tr><tr><td colspan=\"2\">Case 9</td><td/><td/><td/><td/><td/></tr><tr><td>(filler)</td><td/><td colspan=\"3\">(Keyword spotting)</td><td/><td/><td>Zhang[11]</td></tr><tr><td>Case 1</td><td/><td/><td/><td/><td/><td colspan=\"2\">(syllable)</td><td>(prosodic word)</td></tr><tr><td colspan=\"5\">( (Dialogue system) (intonation phrase) (Pause) (1) 1 =0.04 Grouping, HPG)[4][5] Case 2</td><td colspan=\"2\">0.03</td><td>HMM 0.05</td><td>Bahi[12] (Hierarchical Prosodic Phrase ) (Spontaneous speech) (Speaking style) Bazzi 0.04</td></tr><tr><td>(2)</td><td colspan=\"3\">(Grammar) (Real time) HMM</td><td/><td/><td/><td>[13]</td><td>(syllable, Syl)</td></tr><tr><td colspan=\"8\">Kawahara (prosodic phrase, PPh) ( 1) Kim[15] (prosodic phrase group, PG) (Verification) (prosodic word, PW) Lee C.H.[14] Case 3 (slope) i</td><td>(Keyword extraction) (breath-group)</td></tr><tr><td colspan=\"5\">(Key-phrase detection) B1 B2 B3 B4</td><td colspan=\"3\">(Key-phrase verification) B5</td><td>(Sentence parsing) [16][17]</td></tr><tr><td colspan=\"6\">(sentence verification) (Incremental understanding) Haizhou Li, Bin Ma, and Chin-Hui Lee Case 4 Case 5 (Pitch Reset)</td><td colspan=\"2\">B5 B1 Case 6</td><td>[1] Case 7</td><td>Case 8 [18] (Pitch Reset)</td><td>Case 9</td></tr><tr><td/><td colspan=\"2\">Charpter</td><td/><td colspan=\"2\">3 HPG</td><td/><td>[2]</td></tr><tr><td/><td/><td>B2</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td colspan=\"5\">(Spoken Language Understanding, SLU)</td></tr><tr><td/><td/><td/><td/><td>2</td><td/><td>9</td><td>B</td></tr><tr><td colspan=\"2\">AuToBi</td><td colspan=\"3\">Conkie (Prosodic attribute)</td><td>[20]</td><td/><td>(Knowledge based) [19] (Pitch reset) POS</td><td>[3]</td><td>1</td><td>HMM</td></tr><tr><td>delta</td><td/><td colspan=\"2\">HMM</td><td/><td/><td/></tr><tr><td>[4][5]</td><td/><td/><td colspan=\"5\">(Hierarchical Prosodic Phrase Grouping, HPG) (Prosodic word) Sridhar[21] HMM HMM 1 HPG</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">Erteschik-shir</td><td>[22]</td></tr><tr><td colspan=\"2\">Case 1</td><td>Ali</td><td/><td/><td>1:</td><td colspan=\"2\">[1] Wieland</td><td>[23]</td><td>&gt;</td></tr><tr><td colspan=\"2\">Case 2</td><td/><td/><td colspan=\"2\">Bi-gram</td><td/><td>Beam-search Viterbi</td></tr><tr><td/><td/><td colspan=\"2\">[24]</td><td/><td/><td/><td>[6] Bitar</td></tr><tr><td colspan=\"2\">Case 3</td><td/><td>[25]</td><td/><td/><td/><td>HMM (Prosodic Attributes Extraction)</td></tr><tr><td colspan=\"3\">(Pitch)</td><td>(Intensity)</td><td colspan=\"3\">[7] Rabiner (Duration)</td><td>1989</td></tr><tr><td colspan=\"4\">(HPG) (Prosodic Word Boundary) 2 Case 4 (Pitch reset)</td><td/><td colspan=\"3\">(Boundary Decision Tree) (Prosody word)</td><td>[26]</td><td>MFCC</td></tr></table>"
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                "html": null,
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                "content": "<table><tr><td/><td colspan=\"2\">(False Positive, FP) 4 5 12 13 (c=10 g=16)</td><td colspan=\"5\">(Keyword spotting) (True Negative, TN) (Semantic slot) (Speech act) 6 74.10% 52.90% [14] HTK forced alignment 69.19% HMM</td></tr><tr><td>01 02 03 04</td><td colspan=\"3\">(Pragmatics) 4 4 5 11 12 13 4 5 5 (filler) (c=1 g=8) 4 5 11 12 13 ( ) Num i P PW i 15% (c=10 g=16) 3 5 6-9 12 (c=1 g=8) ( ) Dur i P PW i 3 5 6-9 12 (c=10 g=16) 4 5 6-8 12 Reference _ ( ) Dur Max i P PW i (c=1 g=8) 4 5 6-8 12 (c=10 g=16) Label + SVM _ ( ) Dur Min i P PW i</td><td>(Topic) 6 77.42% 74.10% 75.83% 4 73.04% 74.90% accuracy 68% 71.58% 77.42%</td><td colspan=\"2\">DA pair [23] 69.19% i n 68.45% 80.0% 1 i n Dur ij P \u00a6 j 77.73% recall 2 , ,..., Dur 1 Dur i i Max P P Erteschik-shir (Focus) 58.17% 4 52.94% 54.69% 51.25% 54.01% precision 70.22% { 68.45% 70.14% 49.5% 70.62% 58.17% 80% 1 2 { , ,..., Dur Dur i i Min P P</td><td>} } Dur Dur i n P i n P</td></tr><tr><td>05</td><td colspan=\"2\">Decision Tree + SVM ( ) i Dur PW</td><td>i</td><td>83.51%</td><td>70.95%</td><td colspan=\"2\">85.15% ( B E Pause PW i i i</td><td>)</td></tr><tr><td/><td>(2)</td><td/><td/><td>SVM</td><td/><td/></tr><tr><td>06</td><td>( Syl PW i</td><td>)</td><td>i</td><td/><td/><td>SVM Syl N _ i</td></tr><tr><td/><td/><td/><td/><td/><td>3</td><td/></tr><tr><td>07 08 09 10 11</td><td colspan=\"7\">4: DA pairs 1 ( i Dur Syl 6 7 8 ) TP 2 ( ) i Dur Syl accuracy 100% i i TP FP TN FN 5: DA pairs (accuracy) 1 SVM TP TN 2 ( 6) TP precision TP FP 3: 3 ( ) i Dur Syl i 3 SVM SVM ( 7) Syl e Syl b 1 1 _ _ i i Syl e Syl b (precision) SVM 2 2 _ _ i i 3 3 _ _ i Syl e Syl b i accuracy precision recall 51%~58% 68%~80% 51%~59% 4 ( ) Dur Syl i 4 4 4 _ _ i i Syl e Syl b i (True TP recall TP FN ( 8) 4 5 12 13 (c=1 g=8) 83.38% 70.95% 75.33% Positive, TP) (recall) HPG 76%~83% 58%~71% 75%~85% ( ) i Pause PW i pause pause e b</td></tr><tr><td>12 13</td><td colspan=\"7\">bpause (False Negative, FN) 2498 (True Positive, TP) SVM accuracy 850 precision 77.16% 57.83% 76.65% 58.42% Edinburgh Working Papers in Cognitive Science, 11:1-22, 1995. epause 13 12-13 13 660 2: (c=1 g=8) 4 5 6-8 12 (c=10 g=16) [2] N. Chater, M. Pickering, and D. Milward. \"What is incremental interpretation? \" recall 68.25% 75.22% 4 5 12 13 (c=1 g=8) 80.47% 65.02% ISCAS 1998. 75.33% 4 5 6-8 12 Feature-Based System for Automatic Phoneme Recognition in Continuous Speech,\" (c=10 g=16) 80.61% 63.00% 84.00% [1] Ali, J. Van der Spiegel, P. Mueller, G. Haentjens ,and J. Berman, \"An Acoustic-Phonetic 3 5 6-9 12 211 (3) 173 1061 211 58% (c=1 g=8) 82.45% 66.33% 85.15% 3 5 6-9 12 850 11 52 247 568 73 (1) SVM SVM 2 3-5% 58% 4 5 12 13 (c=10 g=16) 81.40% 65.41% 78.03% 4 5 11 12 13 83.51% 70.83% ( ) i pos PW i B i E 75.56% (c=1 g=8) (NSC 80% 10 8 4 5 11 12 13 (c=10 g=16) 81.35% 64.91% 77.48% 99-2221-E-415-006-MY3) . ( ) N Speech N</td></tr></table>"
            }
        }
    }
}