| { |
| "paper_id": "O16-1002", |
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| "date_generated": "2023-01-19T08:05:06.175811Z" |
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| "title": "", |
| "authors": [ |
| { |
| "first": "Kuan-Yu", |
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| "last": "\u9673\u51a0\u5b87", |
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| "last": "Chen", |
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| "email": "kychen@iis.sinica.edu.tw" |
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| "FIGREF0": { |
| "uris": null, |
| "text": "neural networks, DNN)\u8072\u5b78\u6a21\u578b\u53ca\u647a\u7a4d\u795e\u7d93\u7db2\u8def(convolutional neural networks, CNN) \u7d50 \u5408 \u7684 \u5354 \u540c \u6548 \u61c9 \uff0c \u671f \u671b \u589e \u52a0 \u8072 \u5b78 \u6a21 \u578b \u5efa \u6a21 \u4e4b \u4e00 \u822c \u5316 \u80fd \u529b (generalization capability)\u3002(2)\u7531\u65bc\u8a13\u7df4\u591a\u4efb\u52d9\u8072\u5b78\u6a21\u578b\u7684\u904e\u7a0b\u4e2d\uff0c\u8abf\u6574\u4e0d\u540c\u8f14\u52a9\u4efb\u52d9\u4e4b \u8ca2\u737b(\u6b0a\u91cd)\u7684\u65b9\u5f0f\u4e26\u4e0d\u662f\u6700\u4f73\u7684\uff0c\u56e0\u6b64\u6211\u5011\u63d0\u51fa\u4e86\u91cd\u65b0\u8abf\u9069\u6cd5\uff0c\u4ee5\u6e1b\u8f15\u9019\u500b\u554f\u984c\u3002\u6211\u5011 \u57fa\u65bc\u5728\u53f0\u7063\u6240\u6536\u9304\u7684\u83ef\u8a9e\u6703\u8b70\u8a9e\u6599\u5eab(Mandarin meeting recording corpus, MMRC)\u5efa\u7acb \u4e86\u4e00\u7cfb\u5217\u7684\u5be6\u9a57\u3002\u8207\u6578\u7a2e\u73fe\u6709\u7684\u57fa\u790e\u5be6\u9a57\u76f8\u6bd4\uff0c\u5be6\u9a57\u7d50\u679c\u63ed\u793a\u4e86\u6211\u5011\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u4e4b\u6709 \u6548\u6027\u3002 \u95dc\u9375\u8a5e\uff1a\u591a\u4efb\u52d9\u5b78\u7fd2\uff0c\u6df1\u5c64\u5b78\u7fd2\uff0c\u985e\u795e\u7d93\u7db2\u8def\uff0c\u6703\u8b70\u8a9e\u97f3\u8fa8\u8b58\u3002 \u81f4\u8b1d \u672c\u8ad6\u6587\u4e4b\u7814\u7a76\u627f\u8499\u6559\u80b2\u90e8-\u570b\u7acb\u81fa\u7063\u5e2b\u7bc4\u5927\u5b78\u9081\u5411\u9802\u5c16\u5927\u5b78\u8a08\u756b(104-2911-I-003-301) \u8207 \u884c \u653f \u9662 \u79d1 \u6280 \u90e8 \u7814 \u7a76 \u8a08 \u756b (MOST 104-2221-E-003-018-MY3 \u548c MOST 105-2221-E-003-018-MY3)\u4e4b\u7d93\u8cbb\u652f\u6301\uff0c\u8b39\u6b64\u81f4\u8b1d\u3002 \u53c3\u8003\u6587\u737b", |
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| "TABREF0": { |
| "content": "<table><tr><td>The 2016</td></tr><tr><td>\u6458\u8981</td></tr><tr><td>\u8a9e\u97f3\u9577\u4e45\u4ee5\u4f86\u4e00\u76f4\u662f\u4eba\u8ddf\u4eba\u4e4b\u9593\u6700\u81ea\u7136\u7684\u6e9d\u901a\u65b9\u5f0f\uff1b\u5b83\u5728\u672a\u4f86\u5c07\u662f\u4eba\u8207\u96fb\u8166\u7b49\u6a5f\u5668\u9593\u6e9d</td></tr><tr><td>\u901a\u7684\u4e00\u500b\u4e0d\u53ef\u6216\u7f3a\u7684\u91cd\u8981\u5de5\u5177\u3002\u8fd1\u516d\u5341\u5e74\u4f86\uff0c\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u7684\u7814\u7a76\u6d3b\u52d5\u5341\u5206\u6d3b\u8e8d\uff0c\u4e26\u4e14</td></tr><tr><td>\u5df2\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u6210\u529f\u3002\u5728\u7814\u7a76\u521d\u671f\uff0c\u8a9e\u97f3\u8fa8\u8b58\u5668\u53ea\u80fd\u5728\u5b89\u975c\u7684\u74b0\u5883\u4e2d\u8b58\u5225\u4e00\u500b\u55ae\u7368\u7684\u8a5e</td></tr><tr><td>\u5f59\u30021980 \u5e74\u4ee3\uff0c\u4ee5\u9ad8\u65af\u6df7\u5408\u6a21\u578b-\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(Gaussian mixture model-hidden</td></tr><tr><td>Markov model, GMM-HMM)\u505a\u70ba\u8072\u5b78\u6a21\u578b\u4f7f\u5f97\u8a9e\u97f3\u8fa8\u8b58\u6709\u80fd\u529b\u9032\u884c\u5927\u8a5e\u5f59\u91cf\u9023\u7e8c\u8a9e\u97f3</td></tr><tr><td>\u8b58\u5225[1]\u3002\u7531\u65bc GMM-HMM \u7684\u67b6\u69cb\u6613\u65bc\u8a13\u7df4\u6a21\u578b\u548c\u9032\u884c\u8072\u5b78\u89e3\u78bc\uff0c\u56e0\u6b64\u5728\u8fd1\u4e8c\u5341\u5e74\u4f86</td></tr><tr><td>GMM-HMM \u662f\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u7684\u4e3b\u6d41\u8072\u5b78\u6a21\u578b\uff0c\u8072\u5b78\u6a21\u578b\u7684\u7814\u7a76\u4e3b\u8981\u96c6\u4e2d\u5728\u4ee5\u66f4\u597d</td></tr><tr><td>\u7684\u6a21\u578b\u7d50\u69cb\u8207\u8a13\u7df4\u6f14\u7b97\u6cd5\u6539\u826f GMM-HMM[1][2][3][4]\u3002\u5728\u904e\u53bb\u7684\u4e94\u5e74\u5167\uff0c\u6211\u5011\u770b\u898b\u4e86</td></tr><tr><td>\u6df1\u5c64\u5b78\u7fd2\u67b6\u69cb\u548c\u6280\u8853\u5728\u8a9e\u97f3\u9818\u57df\u7684\u7a81\u7834\u6027\u7684\u767c\u5c55\u548c\u5353\u8d8a\u7684\u6210\u6548[5][6][7]\u3002\u6df1\u5c64\u985e\u795e\u7d93\u7db2</td></tr><tr><td>\u8def\u8207\u5176\u8b8a\u9ad4\u6700\u7d42\u53d6\u4ee3\u4e86\u9ad8\u65af\u6df7\u5408\u6a21\u578b\uff1b\u6642\u4e0b\u7684\u6df7\u5408\u6df1\u5c64\u985e\u795e\u7d93\u7db2\u8def-\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b</td></tr><tr><td>(hybrid deep neural networks-hidden Markov model, DNN-HMM)\u5df2\u6210\u70ba\u5927\u591a\u6578\u81ea\u52d5\u8a9e\u97f3</td></tr><tr><td>\u8fa8\u8b58\u7cfb\u7d71\u7684\u8072\u5b78\u6a21\u578b[8][9][10]\u3002\u96d6\u7136\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u6280\u8853\u5df2\u7d93\u662f\u4e00\u9805\u6210\u719f\u7684\u6280\u8853\uff0c\u4f46\u662f\u5728</td></tr><tr><td>\u5be6\u969b\u61c9\u7528\u4e0a\u4ecd\u6709\u8a31\u591a\u554f\u984c\u9700\u8981\u88ab\u89e3\u6c7a\u3002\u4f8b\u5982\u4f7f\u7528\u667a\u6167\u578b\u624b\u6a5f\u9304\u97f3\u6642\u5f80\u5f80\u96e2\u624b\u6a5f\u9ea5\u514b\u98a8\u8f03</td></tr><tr><td>\u9060\uff0c\u9304\u97f3\u54c1\u8cea\u5bb9\u6613\u53d7\u74b0\u5883\u5f71\u97ff\u3002\u6b64\u5916\uff0c\u73fe\u4eca\u8a9e\u97f3\u8fa8\u8b58\u9818\u57df\u4e5f\u9762\u81e8\u8457\u6d77\u91cf\u8a5e\u5f59\u3001\u81ea\u7531\u4e0d\u53d7</td></tr></table>", |
| "text": "Conference on Computational Linguistics and Speech Processing ROCLING 2016, pp. 4-6 \uf0d3 The Association for Computational Linguistics and Chinese Language Processing", |
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| } |
| } |
| } |
| } |
