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{
"paper_id": "O12-1030",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:03:02.365925Z"
},
"title": "Implementation and Comparison of Keyword Spotting for Taiwanese",
"authors": [
{
"first": "Chung-Che",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Che-Hsuan",
"middle": [],
"last": "Chou",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Liang-Yu",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Yu-Jhe",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {},
"email": "liyujhe@mirlab.org"
},
{
"first": "Jyh-Shing",
"middle": [
"Roger"
],
"last": "Jang",
"suffix": "",
"affiliation": {},
"email": "jang@mirlab.org"
},
{
"first": "Hsun-Cheng",
"middle": [],
"last": "Hu",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Shih-Peng",
"middle": [],
"last": "Lin",
"suffix": "",
"affiliation": {},
"email": "shihpeng@iii.org.tw"
},
{
"first": "You-Lian",
"middle": [],
"last": "Huang",
"suffix": "",
"affiliation": {},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "",
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{
"text": "This paper focuses on improving in the performance of a Taiwanese keyword spotting system by integrating speech assessment and pitch contour classification. In the first part of this research, we use different methods to implement a Taiwanese keyword spotting system. In second part, we improve the system by validation using speech assessment and pitch contour classification. Two methods are adopted in the first part to implement the keyword spotting system: hidden Markov model and phone mismatching method. We then perform speech assessment and pitch contour classification to validate the candidate keywords selected by these two methods to refine the results. A threshold is used for a decision tree to make the final decision. Experimental results shows that the equal error rates (ERRs) reduce about 20% and 5% after being incorporated speech assessment validation. After being incorporated with pitch contour classification, ERRs further reduce about 1%. This concludes that the validation technique using speech assessment and pitch contour classification can improve the performance of Taiwanese keyword spotting. 3. ",
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"section": "Abstract",
"sec_num": null
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{
"text": "EQUATION",
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"section": "4.",
"sec_num": null
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{
"text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
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"ref_entries": {
"TABREF1": {
"text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td>indicator</td><td/><td>log</td></tr><tr><td/><td/><td/><td>[17]</td></tr><tr><td>(</td><td>)</td><td/></tr><tr><td>Pr</td><td/><td/></tr><tr><td/><td/><td/><td>filler-model</td></tr><tr><td>2.</td><td/><td/></tr><tr><td/><td/><td>[2]</td></tr><tr><td/><td>18</td><td>61</td></tr><tr><td>fi_init</td><td>fi_final</td><td/></tr><tr><td>i</td><td>I D</td><td>[2]</td></tr><tr><td colspan=\"3\">Keywords: Keywords spotting, hidden Markov model, penalty matrix</td></tr><tr><td/><td/><td/><td>3C</td></tr><tr><td colspan=\"2\">3C</td><td/></tr><tr><td/><td>[1]</td><td/></tr><tr><td colspan=\"2\">Phone Mismatch Penalty Matrix</td><td colspan=\"2\">confidence measure, CM development</td></tr><tr><td/><td/><td>substitution</td><td>insertion</td></tr><tr><td>deletion</td><td/><td/></tr><tr><td/><td>[1]</td><td/></tr><tr><td>1.</td><td/><td/></tr></table>"
},
"TABREF2": {
"text": "Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing(ROCLING 2012)",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td colspan=\"11\">Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012)</td></tr><tr><td>1.</td><td/><td/><td>(</td><td>70</td><td colspan=\"7\">log-likelihood 97 539 692 533 2 ) \u03d2 70 HMM phone sequence</td><td>1</td><td>4 GMM (3)</td></tr><tr><td colspan=\"2\">2. FAR</td><td>\u03d2 rank</td><td colspan=\"2\">1.8%</td><td>46.5%</td><td colspan=\"2\">a b</td><td/><td>\u03d2</td><td colspan=\"2\">26.5% a b</td><td>p</td></tr><tr><td>2.</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td colspan=\"3\">34% 39.4%</td><td/><td/><td/><td/><td/><td colspan=\"2\">28.4% 34.6%</td></tr><tr><td/><td/><td/><td/><td/><td>HMM</td><td/><td colspan=\"3\">27.3% 33.7%</td><td/><td>decision</td></tr><tr><td colspan=\"2\">tree</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>3.</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>[11]</td></tr><tr><td>3.</td><td>7</td><td/><td>[14]</td><td/><td/><td colspan=\"2\">-p -t -k -h</td><td>1</td><td/><td>7</td><td>1</td><td>5</td><td>8</td><td>9</td><td>6</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>[4]</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">3.5 4 4.5</td><td>5</td><td/><td colspan=\"2\">4.5</td></tr><tr><td/><td/><td/><td/><td/><td>HMM</td><td/><td/><td/><td/><td/><td>DET</td></tr><tr><td colspan=\"7\">4. 5.6% false acceptance rate , FAR [16] HMM likelihood ratio testing 4.8% baseline duration</td><td colspan=\"5\">false rejection rate ,FRR [16] equal error rate, EER [16] ( ) HMM 20% time duration baseline</td><td>LRT pause</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>1</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">4</td><td/><td/><td/><td/><td>20%</td></tr><tr><td/><td/><td/><td/><td>[7]</td><td/><td/><td/><td/><td/><td/><td>70</td><td>50</td><td>development</td></tr><tr><td>4.</td><td colspan=\"3\">log-likelihood</td><td/><td colspan=\"3\">rank ratio score -8</td><td/><td/><td/></tr><tr><td colspan=\"3\">1. ForPA</td><td colspan=\"9\">rank ratio score [12][13] penalty -20 NSC 99-2221-E-007 -049 -MY3 50.32%</td><td>confidence measure</td></tr><tr><td colspan=\"5\">D (P , programming, DP (development) 1.8%</td><td colspan=\"3\">) UPDUDP[9]</td><td/><td colspan=\"3\">UPDUDP[9] FRR</td><td>FRR</td><td>FAR Dynamic</td></tr><tr><td colspan=\"10\">AMDF[10] (average magnitude difference function ) 34%</td><td/><td>FAR</td><td>FAR</td><td>DP</td></tr><tr><td>P</td><td/><td/><td/><td/><td>1.1%</td><td colspan=\"3\">log-likelihood HTK</td><td colspan=\"2\">Q 0.9%</td></tr><tr><td colspan=\"2\">(n) C 0 1</td><td/><td/><td/><td/><td/><td>n</td><td/><td>i</td><td>i</td><td>row</td><td>[8]</td><td>Gaussian mixture model, HMM</td></tr><tr><td colspan=\"3\">GMM</td><td colspan=\"3\">column</td><td/><td>[15]</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">(2) (3) HMM</td><td/><td/><td/></tr><tr><td>2.</td><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"3\">backtracking</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>(2)</td></tr><tr><td>1.</td><td/><td/><td/><td/><td colspan=\"2\">ForPA</td><td colspan=\"4\">lau2_heh7 ForPA</td><td>1</td><td>4</td></tr></table>"
}
}
}
} |