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| "abstract": "\u8fd1\u5e74\u4f86\uff0c\u985e\u795e\u7d93\u7db2\u8def (Neural Network) \u5728\u8a9e\u97f3\u8fa8\u8b58\u4e0a\u7684\u7814\u7a76\u6709\u8457\u8c50\u78a9\u7684\u6210\u679c\uff0c\u6709 \u6548\u5730\u6e1b\u5c11\u74b0\u5883\u4ee5\u53ca\u8a9e\u8005\u8b8a\u7570\u5c0d\u8a9e\u97f3\u8a0a\u865f\u9020\u6210\u7684\u5f71\u97ff\uff0c\u5927\u5e45\u63d0\u5347\u8fa8\u8b58\u7387\uff0c\u4f46\u7cfb\u7d71\u7684\u8a9e\u97f3\u8fa8 \u8b58\u80fd\u529b\u4ecd\u6709\u6539\u5584\u7a7a\u9593\u3002\u672c\u8ad6\u6587\u5373\u63d0\u51fa\u65b0\u7684\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u67b6\u69cb\uff0c\u7d50\u5408 Environment Clustering (EC)\u3001Mixture of Experts \u8207\u985e\u795e\u7d93\u7db2\u8def\u4ee5\u9032\u4e00\u6b65\u63d0\u5347\u7cfb\u7d71\u6548\u80fd\u3002\u6211\u5011\u5c07\u8fa8\u8b58 \u7cfb\u7d71\u5206\u70ba Offline \u8207 Online \u5169\u968e\u6bb5\uff1aOffline \u968e\u6bb5\u4f9d\u64da\u8072\u5b78\u7279\u6027\u5c07\u6574\u500b\u8a13\u7df4\u8cc7\u6599\u96c6\u5206\u5272\u6210 \u591a\u500b\u5b50\u8a13\u7df4\u8cc7\u6599\u96c6\uff0c\u4e26\u5efa\u7acb\u5404\u5b50\u8a13\u7df4\u8cc7\u6599\u96c6\u7684\u985e\u795e\u7d93\u7db2\u8def(\u4ee5\u985e\u795e\u7d93\u5b50\u7db2\u8def\u7a31\u4e4b)\u3002Online \u968e\u6bb5\u5247\u4f7f\u7528 GMM-gate \u4f86\u63a7\u5236\u985e\u795e\u7d93\u5b50\u7db2\u8def\u7684\u8f38\u51fa\u3002\u65b0\u63d0\u51fa\u7684\u7cfb\u7d71\u67b6\u69cb\u4fdd\u7559\u5b50\u8a13\u7df4\u8cc7\u6599 \u96c6\u7684\u8072\u5b78\u7279\u6027\uff0c\u5f37\u5065\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u3002\u5be6\u9a57\u4e0a\uff0c\u6211\u5011\u4f7f\u7528 Aurora 2 \u9023\u7e8c\u6578\u5b57\u8a9e\u97f3\u8cc7\u6599\u5eab\uff0c \u4f9d\u64da\u5b57\u932f\u8aa4\u7387(word error rate, WER)\u6bd4\u8f03\u6211\u5011\u63d0\u51fa\u7684\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u67b6\u69cb\u8207\u50b3\u7d71\u4ee5\u985e\u795e\u7d93 \u7db2\u8def\u5efa\u7acb\u7684\u8fa8\u8b58\u7cfb\u7d71\uff0c\u5e73\u5747\u5b57\u932f\u8aa4\u7387\u9032\u6b65 5.9% \uff0c\u7531 5.25%\u964d\u4f4e\u81f3 4.94%\u3002", |
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| "text": "EQUATION", |
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| "text": "\u5716\u4e09\u3001Sigmoid\u3001Tanh \u53ca Rectified Linear ", |
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| "text": "% 100 \uf0b4 \uf02b \uf02b \uf03d N I D S WER ( 9 ) \u5728 \u5b57 \u4e32 \u6bd4 \u5c0d \u4e2d \uff0c \u5169 \u500b \u5b57 \u4e32 \u53ef \u80fd \u6703 \u767c \u751f \u63d2 \u5165 (Insertion) \u3001 \u522a \u9664 (Deletion) \u4ee5 \u53ca \u66ff \u63db (", |
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| "sec_num": null |
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| { |
| "text": "EQUATION", |
| "cite_spans": [], |
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| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "raw_str": "\uf0e5 \uf02d \uf02d i i i T i i w X X ) ( ) ( min arg w t w t ( 10 ) \u5176\u4e2d i X \u70ba\u7b2c i \u7b46\u8cc7\u6599\u7d93\u904e\u5168\u90e8 7 \u500b\u985e\u795e\u7d93\u7db2\u8def\u7684\u8f38\u51fa\u3001 i t \u70ba\u7b2c i \u7b46\u8cc7\u6599\u7d93\u904e\u6b63\u78ba\u985e\u795e\u7d93 \u7db2\u8def\u7684\u8f38\u51fa\u3001 w \u70ba\u6b32\u6c42\u5f97\u7684\u7d44\u5408\u4fc2\u6578\u3002\u5c0d w \u6c42\u89e3\u4e26\u52a0\u5165\u4e00\u822c\u5316\u9805\u53ef\u5beb\u70ba\uff1a ) ( ) ( 1 \uf0e5 \uf0e5 \uf02d \uf02b \uf03d i i T i i i T i X I X X t w \uf064", |
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| "BIBREF0": { |
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| "content": "<table><tr><td>Training Stage</td><td/></tr><tr><td>Feature</td><td>Model</td></tr><tr><td>Extraction</td><td>Training</td></tr><tr><td>Training Speech</td><td/></tr><tr><td>Acoustic</td><td>Language</td></tr><tr><td>Model</td><td>Model</td></tr><tr><td>Testing Stage</td><td/></tr><tr><td colspan=\"2\">\u795e\u7d93\u7db2\u8def\u7522\u751f\u5206\u5225\u70ba\u76ee\u6a19\u8a0a\u865f\u8207\u5e72\u64fe\u8a0a\u865f\u7684\u5169\u500b\u8f38\u51fa\uff0c\u4f7f\u7528\u5206\u96e2\u7684\u7d50\u679c\u9032\u884c\u8fa8\u8b58[7]\u7b49 \u7b49\u8a31\u591a\u65b9\u5f0f\u3002\u5728\u9019\u4e9b\u76f8\u95dc\u7814\u7a76\u4e2d\uff0c\u90fd\u662f\u4f7f\u7528\u540c\u4e00\u500b\u985e\u795e\u7d93\u7db2\u8def\u4f86\u8655\u7406\u6240\u6709\u74b0\u5883\u7684\u60c5\u6cc1\u3002 Output Sentence Feature Extraction Decode</td></tr><tr><td colspan=\"2\">\u5728\u6574\u9ad4\u5b78\u7fd2(ensemble learning)\u7684\u76f8\u95dc\u7814\u7a76\u4e2d\uff0c\u6709\u4f7f\u7528 bagging[8]\u6216\u662f boosting[9]\u7b49\u7b49\u65b9</td></tr><tr><td colspan=\"2\">\u5f0f\uff0c\u9019\u88e1\u6211\u5011\u4f7f\u7528\u57fa\u65bc Environment Clustering (EC)[10]\u53ca Mixture of Experts[11]\u7684\u67b6\u69cb Testing Speech</td></tr><tr><td colspan=\"2\">\u4f86\u8a13\u7df4\u591a\u500b\u985e\u795e\u7d93\u7db2\u8def\uff0c\u4e26\u5728\u6700\u5f8c\u9078\u64c7\u4e00\u500b\u9069\u7576\u7684\u985e\u795e\u7d93\u7db2\u8def\u9032\u884c\u8f38\u51fa\u3002 \u5728\u63a5\u4e0b\u4f86\u7684\u5167\u5bb9\uff0c\u7b2c\u4e8c\u7ae0\u5c07\u4ecb\u7d39\u6574\u500b\u8a9e\u97f3\u8fa8\u8b58\u7684\u4e3b\u8981\u6d41\u7a0b\u4ee5\u53ca\u4e00\u4e9b\u76f8\u95dc\u7684\u7814\u7a76\u65b9 \u5716\u4e00\u3001\u8a9e\u8005\u8fa8\u8b58\u6d41\u7a0b\u5716</td></tr><tr><td colspan=\"2\">\u6cd5\u3002\u7b2c\u4e09\u7ae0\u5c07\u4ecb\u7d39\u672c\u7bc7\u8ad6\u6587\u7684\u7cfb\u7d71\u67b6\u69cb\u3002\u7b2c\u56db\u7ae0\u70ba\u5be6\u9a57\u7684\u90e8\u5206\uff0c\u6b64\u7ae0\u7bc0\u5305\u542b\u4ecb\u7d39\u5be6\u9a57\u8a9e</td></tr><tr><td colspan=\"2\">\u6599\u8207\u5be6\u9a57\u8a2d\u5b9a\u3001baseline \u7cfb\u7d71\u4ee5\u53ca\u672c\u8ad6\u6587\u7cfb\u7d71\u7684 Word Error Rate (WER)\u3002\u7b2c\u4e94\u7ae0\u70ba\u6b64\u7814 \u7a76\u7684\u7d50\u8ad6\u3002 (\u4e8c)\u3001\u9ad8\u65af\u6df7\u5408\u6a21\u578b(Gaussian Mixture Model, GMM)</td></tr><tr><td>\u4e8c\u3001\u8a9e\u97f3\u8fa8\u8b58\u6d41\u7a0b\u53ca\u76f8\u95dc\u7814\u7a76\u65b9\u6cd5\u4ecb\u7d39</td><td/></tr><tr><td colspan=\"2\">\u5728\u6b64\u7ae0\u7bc0\u4e2d\u6211\u5011\u5c07\u7c21\u55ae\u4ecb\u7d39\u57fa\u672c\u7684\u8a9e\u97f3\u8fa8\u8b58\u6d41\u7a0b\uff0c\u53ca\u8fa8\u8b58\u4e2d\u6240\u4f7f\u7528\u7684\u9ad8\u65af\u6df7\u5408\u6a21\u578b</td></tr><tr><td colspan=\"2\">(Gaussian Mixture Model\uff0cGMM) \u8207\u985e\u795e\u7d93\u7db2\u8def(Neural Network)\u3002</td></tr><tr><td>(\u4e00)\u3001\u8a9e\u97f3\u8fa8\u8b58\u6d41\u7a0b</td><td/></tr><tr><td colspan=\"2\">\u5716\u4e00\u70ba\u4e00\u500b\u57fa\u672c\u7684\u8a9e\u97f3\u8fa8\u8b58\u6d41\u7a0b\uff0c\u53ef\u5206\u70ba\u8a13\u7df4\u53ca\u6e2c\u8a66\u968e\u6bb5\u3002\u9996\u5148\u64f7\u53d6\u8a9e\u97f3\u8a0a\u865f\u7684\u7279</td></tr><tr><td colspan=\"2\">\u5fb5(feature extraction)\uff0c\u5982\u6885\u723e\u5012\u983b\u8b5c\u4fc2\u6578(Mel-Frequency Cepstral Coefficients, MFCC)\uff1b</td></tr><tr><td colspan=\"2\">\u63a5\u8457\u5229\u7528\u64f7\u53d6\u7684\u8a9e\u97f3\u7279\u5fb5\u5728\u8a13\u7df4\u968e\u6bb5\u8a13\u7df4\u6a21\u578b(model training)\uff0c\u6216\u5728\u6e2c\u8a66\u968e\u6bb5\u89e3\u78bc</td></tr><tr><td colspan=\"2\">(decode) \u70ba \u6587 \u5b57 \u3002 \u8a13 \u7df4 \u968e \u6bb5 \u5c07 \u7522 \u751f \u8072 \u5b78 \u6a21 \u578b (acoustic model) \u53ca\u8a9e\u8a00\u6a21\u578b(language</td></tr><tr><td colspan=\"2\">model)\uff0c\u4e26\u4f9b\u7d66\u6e2c\u8a66\u968e\u6bb5\u89e3\u78bc\u4f7f\u7528\u3002\u6b64\u5916\uff0c\u76ee\u524d\u8a13\u7df4\u8072\u5b78\u6a21\u578b\u7684\u65b9\u5f0f\u4e3b\u8981\u70ba GMM \u8207\u985e</td></tr><tr><td>\u795e\u7d93\u7db2\u8def\uff0c\u5c07\u65bc\u4e0b\u4e00\u7bc0\u4ecb\u7d39\u3002</td><td/></tr></table>", |
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| "text": "The 2015 Conference on Computational Linguistics and Speech Processing ROCLING 2015, pp. 136-147 \uf0d3 The Association for Computational Linguistics and Chinese Language Processing \u4e00\u3001\u7c21\u4ecb \u96d6\u7136\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u5728\u5b89\u975c\u74b0\u5883\u4e0b\u53ef\u4ee5\u9054\u5230\u4e0d\u932f\u7684\u8fa8\u8b58\u7387\uff0c\u4f46\u662f\u5728\u5be6\u969b\u61c9\u7528\u4e0a\uff0c\u7531\u65bc \u74b0\u5883\u566a\u97f3(environment noise) \u7522 \u751f \u7684 \u52a0 \u6210 \u6027 \u96dc \u8a0a (additive noise) \u53ca \u901a \u9053 \u5931 \u771f (channel distortion)\u7522\u751f\u7684\u5377\u7a4d\u6027\u96dc\u8a0a(convolutive noise)\u7b49\u60c5\u6cc1\uff0c\u9020\u6210\u8a13\u7df4\u53ca\u6e2c\u8a66\u8a9e\u6599\u7684\u74b0\u5883\u4e0d\u5339 \u914d\u554f\u984c\uff0c\u9650\u5236\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u7684\u6548\u80fd\u3002 \u6b32\u89e3\u6c7a\u4e0a\u8ff0\u7684\u4e0d\u5339\u914d\u554f\u984c\uff0c\u5728\u6a21\u578b\u7a7a\u9593(model space)\u7684\u8655\u7406\u4e2d\u6709\u8a31\u591a\u6a21\u578b\u8abf\u9069 (model adaptation)\u7684\u65b9\u5f0f\uff0c\u4f8b\u5982\u6700\u5927\u5f8c\u9a57\u6a5f\u7387\u4f30\u8a08(maximum a posteriori estimation)[1]\u3001 \u6700\u5927\u4f3c\u7136\u7dda\u6027\u8ff4\u6b78(maximum likelihood linear regression)[2]\u3001\u6700\u5c0f\u5206\u985e\u932f\u8aa4\u7dda\u6027\u56de\u6b78 (minimum classification error linear regression)[3]\u7b49\u7b49\u3002 \u5728\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\u8b58\u4e0a\u5df2\u7d93\u6709\u8a31\u591a\u4f7f\u7528\u7814\u7a76\u4f7f\u7528\u985e\u795e\u7d93\u7db2\u8def\uff0c\u4f8b\u5982\uff0c\u5728\u74b0\u5883\u4e0d\u5339\u914d\u7684 \u60c5\u6cc1\u4e0b\u4f7f\u7528\u7dda\u6027\u8f49\u63db\u5f37\u5065\u6a21\u578b[4][5]\uff1b\u7d50\u5408 GMM-HMM \u8207 DNN-HMM \u9032\u884c\u8f38\u51fa[6]\uff1b\u985e" |
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| "content": "<table><tr><td colspan=\"7\">\u6b64\u5916\uff0c\u4e00\u500b\u5b8c\u6574\u7684\u985e\u795e\u7d93\u7db2\u8def\u70ba\u591a\u500b\u795e\u7d93\u5143\u67b6\u69cb\u800c\u6210\uff0c\u5982\u5716\u4e94\u70ba\u96d9\u96b1\u85cf\u5c64( hidden layer) \u7684\u985e\u795e\u7d93\u7db2\u8def\uff0c\u7e3d\u5171\u7531\u4e94\u500b\u795e\u7d93\u5143\u7d44\u6210(\u7b2c\u4e00\u5c64\u6709\u4e09\u500b\u795e\u7d93\u5143\u7bc0\u9ede\uff0c\u7b2c\u4e8c\u5c64\u5247\u70ba\u5169\u500b\u795e \u7684\u8072\u5b78\u7279\u6027\u5206\u985e\u8a13\u7df4\u8cc7\u6599\u593e\u70ba\u516d\u500b\u5b50\u8a13\u7df4\u8cc7\u6599\u96c6\uff0c\u4e26\u5206\u5225\u5c0d ALL NN \u9032\u884c\u5012\u50b3\u905e\u8abf\u6574\u7db2\u8def \u56db\u3001\u5be6\u9a57\u8207\u7d50\u679c HMM observation probability \u53c3\u6578\uff0c\u5f97\u5230\u516d\u500b\u985e\u795e\u7d93\u5b50\u7db2\u8def male NN \u3001 female NN \u3001 FH NN \u3001 FL NN \u3001 MH NN \u4ee5\u53ca NN \u3002 ML \u7d93\u5143\u7bc0\u9ede)\u3002\u8cc7\u6599\u8f38\u5165\u81f3\u7b2c\u4e00\u5c64\u7684\u795e\u7d93\u5143\u7684\uff0c\u800c\u7b2c\u4e8c\u5c64\u7684\u8f38\u5165\u5247\u70ba\u7b2c\u4e00\u5c64\u7684\u8f38\u51fa\u3002\u5176\u4e2d \u7684\u53c3\u6578\uf07b \uf07d n i w i , ... , 2 , 1 | \uf03d \u8207b \u53ef\u7531\u5012\u50b3\u905e(back propagation)\u8a13\u7df4\u800c\u5f97\uff1b\u8a73\u7d30\u7684\u7db2\u8def\u8a13\u7df4\u6d41 \u7a0b\u53ef\u53c3\u8003[12]\u3002 Control Gating function GMM \u3001 female GMM \u3001 FH GMM \u3001 FL GMM \u3001 \u5728\u672c\u7bc0\uff0c\u6211\u5011\u5c07\u4ecb\u7d39\u5be6\u9a57\u7684\u8a2d\u5b9a\u3001\u4e26\u5206\u6790\u6bd4\u8f03\u50b3\u7d71\u5229\u7528\u985e\u795e\u7d93\u7db2\u8def\u6a21\u578b\u7684\u8fa8\u8b58\u7cfb\u7d71 \u53e6\u5916\uff0cGMM \u6a21\u578b\u7684\u8a13\u7df4\uff0c\u9996\u5148\u4ee5\u8a13\u7df4\u8cc7\u6599\u96c6\u4f9d\u64da\u5f0f(1)male \u4ee5\u53ca\u672c\u6587\u63d0\u51fa\u7684\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u7684\u7d50\u679c\u3002</td></tr><tr><td>MH GMM \u8207</td><td colspan=\"2\">ML GMM \u3002</td><td/><td/><td/></tr><tr><td colspan=\"4\">x 1 (\u4e00)\u3001\u5be6\u9a57\u8a9e\u97f3\u8cc7\u6599\u8207\u5be6\u9a57\u8a2d\u5b9a</td><td/><td/></tr><tr><td colspan=\"7\">x 2 (\u4e8c)\u3001Online \u7cfb\u7d71\u5efa\u69cb \u8a9e\u97f3\u8fa8\u8b58\u7684\u5be6\u9a57\uff0c\u6211\u5011\u4f7f\u7528 Kaldi \u9019\u5957\u7528\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u958b\u653e\u539f\u59cb\u78bc\u5de5\u5177[14] \uff0c\u4e26\u505a y 1 NN 1 NN 2 NN 7 \u2027\u2027\u2027 \u70ba\u6211\u5011\u7684 baseline NN-HMM \u7cfb\u7d71\uff1b\u4e26\u4ee5 Aurora 2 \u8cc7\u6599\u5eab[15] \u505a\u70ba\u672c\u5be6\u9a57\u7684\u8a9e\u6599\u5eab\u3002 \u524d\u4e00\u5c0f\u7bc0\u5f97\u5230\u7684\u6574\u9ad4\u6a21\u578b\u53ca\u516d\u500b\u5b50\u96c6\u6a21\u578b\uff0c\u5c07\u63d0\u4f9b\u7d66 online \u968e\u6bb5\u4f7f\u7528\u3002\u5982\u5716\u4e03\u6240 Aurora 2 \u70ba\u4e00\u500b\u82f1\u6587\u9023\u7e8c\u6578\u5b57\u8a9e\u97f3\u7684\u8cc7\u6599\u5eab\uff0c\u5305\u542b\u516b\u7a2e\u4e0d\u540c\u7684\u52a0\u6210\u6027\u96dc\u8a0a\u74b0\u5883(Subway, \u793a\uff0c\u5728 online \u968e\u6bb5\u6642\uff0c\u6211\u5011\u5c07\u6574\u53e5\u6e2c\u8a66\u8cc7\u6599\u5229\u7528\u5f0f(1)\uff0c\u5206\u5225\u8a08\u7b97\u5404\u5b50\u96c6 GMM \u6a21\u578b\u7684 \u4e03\u500b\u5e73\u5747\u5f8c\u9a57\u6a5f\u7387\uff0c\u5f97\u5230\u4e03\u500b\u5e73\u5747\u5f8c\u9a57\u6a5f\u7387 7 2 1 , ... , , p p p Babble, Car, Exhibition, Airport, Street, Train Station, Restaurant)\u3001\u5169\u7a2e\u4e0d\u540c\u7684\u901a\u9053\u96dc\u8a0a \uff0c\u4e26\u6c7a\u5b9a\u5176\u4e2d\u7684\u6700\u5927\u503c\u8207\u76f8\u5c0d (G712 and MIRS) \u8207\u4e03\u7a2e\u4e0d\u540c\u7684 SNR (clean, 20 dB, 15 dB, 10 dB, 5 dB, 0 dB, -5 dB)\u3002\u8a9e</td></tr><tr><td colspan=\"7\">y 2 \u6599\u5eab\u4e2d\uff0c\u542b\u6709\u96dc\u8a0a\u7684\u8a9e\u97f3\u70ba\u4eba\u5de5\u6dfb\u52a0\u4e0d\u540c\u7684\u96dc\u8a0a\u74b0\u5883\u8207 SNR \u5230\u4e7e\u6de8\u8a9e\u97f3\u4e0a\u3002\u53e6\u5916\uff0cAurora \u61c9\u7684\u7b2c i \u500b\u5b50\u96c6\uff0c\u5176\u4e2d i \u70ba\uff1a</td></tr><tr><td colspan=\"7\">x 3 \u4e09\u3001\u672c\u8ad6\u6587\u7cfb\u7d71\u67b6\u69cb \u540c\u4e00\u53e5\u8a9e\u97f3\u8a0a\u865f\u5728\u4e0d\u540c\u7684\u8a9e\u8005\u3001\u74b0\u5883\u7b49\u7b49\u60c5\u6cc1\u8868\u73fe\u7684\u8072\u5b78\u7279\u6027\u4e0d\u76e1\u76f8\u540c\uff0c\u56e0\u6b64\u53ef\u4f9d Layer 1 Layer 2 \u5716\u56db\u3001\u96d9\u96b1\u85cf\u5c64\u985e\u795e\u7d93\u7db2\u8def\u793a\u610f\u5716 \u64da\u4e0d\u540c\u7684\u8072\u5b78\u5206\u985e\u65b9\u5f0f\uff0c\u4f8b\u5982\u6027\u5225\u3001\u8a0a\u566a\u6bd4(signal-to-noise ratio\uff0cSNR)\u7b49\u7b49\uff0c\u5c07\u4e00\u4efd\u8a13 \u7df4\u8a9e\u6599\u5eab\u5206\u5272\u6210\u6578\u7a2e\u4e0d\u540c\u7684\u5b50\u96c6\uff0c\u4e26\u4ee5\u985e\u795e\u7d93\u7db2\u8def\u8207 GMM \u6a21\u578b\u5316\u6bcf\u4e00\u500b\u5b50\u96c6\u6240\u4ee3\u8868\u7684 \u8072\u5b78\u7279\u6027\u3002\u6e2c\u8a66\u6642\uff0c\u9996\u5148\u4ee5 GMM \u6a21\u578b\u6c7a\u5b9a\u6e2c\u8a66\u8a9e\u6599\u7684\u985e\u5225\uff0c\u518d\u4f9d\u64da\u5176\u7d50\u679c\uff0c\u9078\u64c7\u76f8\u5c0d \u61c9\u7684\u985e\u795e\u7d93\u7db2\u8def\u6a21\u578b\uff0c\u6700\u5f8c\u5f97\u5230\u8f03\u5177\u4ee3\u8868\u6027\u7684\u8a9e\u97f3\u7279\u6027\u8f38\u51fa\uff0c\u9032\u800c\u589e\u9032\u8fa8\u8b58\u6548\u679c\u3002 \u6211\u5011\u5c07\u7cfb\u7d71\u5206\u6210 online \u8207 offline \u968e\u6bb5\uff0c\u5716 \u4e94\u5247\u70ba\u4e00 online \u7684\u6d41\u7a0b\u5716\u3002offline \u968e\u6bb5\u4f9d \u64da\u4e0d\u540c\u8072\u5b78\u7279\u6027\u7684\u8cc7\u6599\u96c6\uff0c\u5404\u5225\u8a13\u7df4\u51fa\u5c0d\u61c9\u7684\u985e\u795e\u7d93\u7db2\u8def\uf07b \uf07d n 2 1 NN , ... , NN , NN (\u4ee5\u985e\u795e \u5716\u4e94\u3001\u672c\u8ad6\u6587\u7cfb\u7d71\u67b6\u69cb\u793a\u610f\u5716 (\u4e00)\u3001Offline \u7cfb\u7d71\u5efa\u69cb \u5728 offline \u7cfb\u7d71\u4e2d\uff0c\u6211\u5011\u5c07\u8a13\u7df4\u8cc7\u6599\u96c6\u4f9d\u64da\u6027\u5225\u4ee5\u53ca\u8a0a\u566a\u6bd4\u5206\u6210\u516d\u500b\u5b50\u8a13\u7df4\u8cc7\u6599\u96c6\uff1a \u7537\u6027\u3001\u5973\u6027\u3001\u7537\u6027\u9ad8 SNR\u3001\u7537\u6027\u4f4e SNR\u3001\u5973\u6027\u9ad8 SNR \u4ee5\u53ca\u5973\u6027\u4f4e SNR\uff1b\u5982\u5716\u516d\u6240\u793a\uff1a ALL (NN ALL ) Data 1 Data 2 Data 7 \u2027\u2027\u2027 k k p i 7 ,..., 2 , 1 max arg \uf03d ( 8 ) 2 \u8a9e\u6599\u5eab\u5305\u542b\u8a13\u7df4\u8207\u6e2c\u8a66\u7684\u8a9e\u6599\u96c6\uff1a\u8a13\u7df4\u8a9e\u6599\u5eab\u5305\u542b clean-\u8207 multi-condition \u5169\u7a2e\u8a13\u7df4 \uf03d \u8a9e\u6599\u5eab\uff0c\u672c\u5be6\u9a57\u4f7f\u7528 multi-condition \u8a13\u7df4\u8a9e\u6599\u5eab\u3002\u8a72\u8a9e\u6599\u5eab\u5305\u542b\u56db\u7a2e\u566a\u97f3\u985e\u578b (Subway, \u6700\u5f8c\uff0c\u518d\u7531\u7b2ci \u500b\u5b50\u96c6\u5c0d\u61c9\u7684\u985e\u795e\u7d93\u7db2\u8def\u7684\u8f38\u51fa\u4f5c\u70ba HMM \u7684\u89c0\u6e2c\u6a5f\u7387\u3002 NN 1 NN 2 NN 7 \u2027\u2027\u2027 Control GMM 1 GMM 2 \u2027 \u2027 \u2027 GMM 7 HMM observation probability y 1 y 2 y 7 p 1 p 2 p 7 Babble, Car, Exhibition) (\u4e8c)\u3001\u8a55\u4f30\u65b9\u6cd5</td></tr><tr><td colspan=\"7\">\u7d93\u5b50\u7db2\u8def\u7a31\u4e4b)\uff0c\u4e26\u4f9b\u7d66 online \u968e\u6bb5\u4f7f\u7528\u3002\u53e6\u5916\uff0c\u5728 online \u968e\u6bb5\u4f7f\u7528\u4e00\u500b gating function \u5be6\u9a57\u7d50\u679c\u7684\u8a55\u4f30\u65b9\u9762\uff0c\u6211\u5011\u4f7f\u7528\u5b57\u932f\u8aa4\u7387(Word Error Rate, WER)\u4f86\u8a55\u4f30\u5be6\u9a57\u7d50\u679c\uff0c \u4f86\u9078\u64c7\u985e\u795e\u7d93\u5b50\u7db2\u8def\u7684\u8f38\u51fa\uff0c\u4e26\u5f97\u5230\u6700\u5f8c\u7684\u8fa8\u8b58\u7d50\u679c\u3002\u6700 \u5f8c\uff0c\u6211\u5011\u9078\u64c7 GMM \u505a\u70ba gating function\uff0c\u4ee5 GMM-gate \u7a31\u4e4b\u3002 Male Female High Low High Low \u6211\u5011\u63d0\u51fa\u57fa\u65bc\u74b0\u5883\u7fa4\u96c6(Environment Clustering\uff0cEC)[10]\u4ee5\u53ca mixture of local Male Male Female Female Data \u5176\u8a08\u7b97\u65b9\u5f0f\u5982\u4e0b\u5f0f\uff1a</td></tr><tr><td colspan=\"7\">experts[11]\u7684\u591a\u985e\u795e\u7d93\u5b50\u7db2\u8def\u8a13\u7df4\u53ca\u7d50\u5408\u5404\u5b50\u7db2\u8def\u8f38\u51fa\u4e4b\u67b6\u69cb\uff0c\u4e0b\u4e00\u7bc0\u5c07\u4ecb\u7d39 offline \u7684 \u7cfb\u7d71\u5efa\u69cb\u6d41\u7a0b\u53ca online \u7684\u6e2c\u8a66\u6d41\u7a0b\u3002 SNR SNR SNR SNR \u5716\u4e03\u3001Online \u968e\u6bb5\u67b6\u69cb\u5716</td></tr><tr><td colspan=\"2\">(NN male )</td><td>(NN female )</td><td>(NN MH )</td><td>(NN ML )</td><td colspan=\"2\">(NN FH )</td><td>(NN FL )</td></tr><tr><td/><td/><td/><td colspan=\"2\">\u5716\u516d\u3001EC \u6a39\u67b6\u69cb</td><td/></tr><tr><td colspan=\"6\">\u5176\u4e2d\uff0c\u985e\u795e\u7d93\u5b50\u7db2\u8def\u7684\u8a13\u7df4\uff0c\u9996\u5148\u4ee5\u8a13\u7df4\u8cc7\u6599\u96c6\u8a13\u7df4\u51fa global \u7684</td><td>ALL NN \uff0c\u63a5\u8457\u4f9d\u64da\u4e0d\u540c</td></tr></table>", |
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| "text": "\uff0c\u4f7f\u7528 EM \u6f14\u7b97\u6cd5\u8a13\u7df4 UBM[13] \u6a21\u578b\uff0c ALL GMM \uff0c\u63a5\u8457\u5c0d\u6bcf\u4e00\u7a2e\u5b50\u8a13\u7df4\u8cc7\u6599\u96c6\u4ee5 MAP(Maximum a Posteriori) estimation \u8abf\u9069(adaptation)\u51fa\u516d\u7a2e\u5b50\u96c6 GMM \u6a21\u578b\uff1a \u8207\u4e94\u7a2e SNR (clean, 20 dB, 15 dB, 10 dB, 5 dB)\uff0c\u4e00\u5171\u6709 8440 \u53e5\uff0c \u7e3d\u9577\u5ea6\u7d04\u70ba\u56db\u500b\u5c0f\u6642\uff1b\u6e2c\u8a66\u8a9e\u6599\u96c6\u5247\u5206\u6210\u4e09\u500b\u5b50\u96c6 Set A\u3001Set B \u53ca Set C\uff0c\u5404\u6e2c\u8a66\u5b50\u96c6 \u4e2d\u7686\u6709\u4e0d\u540c\u7684 SNR \u74b0\u5883\uff0c\u5f9e 20 dB \u81f3-5 dB \u8207 clean\u3002Set A \u5305\u542b\u8207\u8a13\u7df4\u8a9e\u6599\u76f8\u540c\u7684\u56db\u7a2e \u566a\u97f3\uff0cSet B \u5247\u70ba\u5305\u542b Restaurant, Street, Airport \u8207 Train Station \u7684\u74b0\u5883\u96dc\u8a0a\uff0cSet C \u70ba\u5169 \u7a2e\u566a\u97f3 (Subway, Street) \u52a0\u4e0a\u901a\u9053\u5931\u771f\u3002 \u6211\u5011\u4f7f\u7528\u6b50\u6d32\u96fb\u4fe1\u6a19\u6e96\u5316\u5354\u6703 (European Telecommunications Standards Institute, ETSI) \u6240\u63d0\u51fa\u7528\u65bc\u9032\u884c\u5206\u6563\u5f0f\u8a9e\u97f3\u8fa8\u8b58\u7684 AFE (Advanced Front-End)\uff0c\u505a\u70ba\u5be6\u9a57\u7528\u7684\u7279 \u5fb5\u3002\u97f3\u6846\u9577\u5ea6\u70ba 25 \u6beb\u79d2\uff0c\u97f3\u6846\u79fb\u52d5\u9577\u5ea6\u70ba 10 \u6beb\u79d2\u3002\u795e\u7d93\u7db2\u8def\u7684\u8a13\u7df4\u4f7f\u7528 13 \u7dad AFE \u52a0 \u4e0a\u5176\u4e00\u968e\u53ca\u4e8c\u968e\u52d5\u614b\u7279\u5fb5\uff0c\u4e26\u524d\u5f8c\u4e32\u63a5 5 \u500b\u97f3\u6846\uff0c\u8f38\u5165\u5411\u91cf\u5171 429 \u7dad\u3002HMM \u6211\u5011\u5b9a\u7fa9 \u975c\u97f3\u70ba 3 \u500b\u72c0\u614b\uff0c\u6578\u5b57\u7684\u8072\u97f3\u70ba 16 \u500b\u72c0\u614b\uff0c\u5171\u6709 179 \u500b\u72c0\u614b\u3002 \u5728\u5be6\u9a57\u4e2d\uff0c\u985e\u795e\u7d93\u5b50\u7db2\u8def\u6211\u5011\u4f7f\u7528\uff11\u5c64\u96b1\u85cf\u5c64\uff0c\u4e00\u5c64\u6709 2560 \u500b\u795e\u7d93\u5143\u3002\u8a13\u7df4\u4f7f\u7528 dropout[16]\u4ee5\u907f\u514d overfitting\u3002\u6b64\u5916\uff0cdropout rate \u70ba 0.8\uff1b\u8a73\u7d30\u7684\u5be6\u9a57\u8a2d\u5b9a\u53ef\u53c3\u8003[17]\u3002" |
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| "content": "<table><tr><td>\u6599\u5e73\u5747\u7684\u6548\u679c\u3002</td><td/><td/><td/><td/></tr><tr><td/><td/><td colspan=\"2\">\u8868\u4e09\u3001\u7dda\u6027\u7d44\u5408\u6cd5\u8207 baseline \u6bd4\u8f03</td><td/></tr><tr><td/><td>Set A</td><td>Set B</td><td>Set C</td><td>Avg.</td></tr><tr><td>Baseline</td><td>4.65</td><td>5.83</td><td>5.28</td><td>5.25</td></tr><tr><td>Linear Combination</td><td>4.78</td><td>5.81</td><td>5.48</td><td>5.33</td></tr><tr><td colspan=\"5\">\u5728\u9032\u884c\u8a9e\u97f3\u8fa8\u8b58\u7684\u5be6\u9a57\u524d\uff0c\u6211\u5011\u9996\u5148\u6e2c\u8a66\u4f7f\u7528 GMM \u4f86\u9032\u884c\u6a21\u578b\u9078\u64c7\u7684\u80fd\u529b\u3002\u5728\u8868</td></tr><tr><td colspan=\"3\">\u56db\u7684\u5be6\u9a57\u4e2d\uff0c\u5206\u5225\u70ba GMM components</td><td colspan=\"2\">Test Error Rate</td></tr><tr><td>GMM</td><td/><td>64</td><td>7.8</td><td/></tr><tr><td>GMM</td><td/><td>128</td><td>7.3</td><td/></tr><tr><td/><td/><td colspan=\"2\">\u8868\u4e94\u3001\u672c\u6587\u65b9\u6cd5\u8207 baseline \u6bd4\u8f03</td><td/></tr><tr><td/><td>Set A</td><td>Set B</td><td>Set C</td><td>Avg.</td></tr><tr><td>Baseline</td><td>4.65</td><td>5.83</td><td>5.28</td><td>5.25</td></tr><tr><td>Proposed method</td><td>4.39</td><td>5.41</td><td>5.10</td><td>4.94</td></tr><tr><td/><td/><td/><td/><td>11 )</td></tr><tr><td colspan=\"5\">\u5247\u6211\u5011\u53ef\u4ee5\u4f7f\u7528 w \u7dda\u6027\u7d44\u5408\u6574\u9ad4\u6a21\u578b\u53ca\u5b50\u96c6\u6a21\u578b\u7684\u8f38\u51fa\uff0c\u5176\u8fa8\u8b58\u7d50\u679c\u5982\u8868\u4e09\u3002\u5f9e\u7d50\u679c\u53ef</td></tr><tr><td colspan=\"5\">\u4ee5\u770b\u51fa\u5176\u6548\u679c\u660e\u986f\u4f4e\u65bc baseline \u7cfb\u7d71\uff0c\u6211\u5011\u63a8\u6e2c\u539f\u56e0\u70ba\u5c0d\u65bc\u6bcf\u7b46\u6e2c\u8a66\u8cc7\u6599\u90fd\u4f7f\u7528\u540c\u4e00\u7d44</td></tr><tr><td colspan=\"5\">\u52a0\u6b0a\u503c\u9032\u884c\u7d44\u5408\uff0c\u6c92\u6709\u8003\u616e\u5230\u6bcf\u7b46\u6e2c\u8a66\u8cc7\u6599\u7684\u7368\u7279\u6027\uff0c\u6574\u500b\u7cfb\u7d71\u53ea\u6703\u5f97\u5230\u5c0d\u65bc\u5404\u985e\u578b\u8cc7</td></tr></table>", |
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| "type_str": "table", |
| "text": "GMM \u5206\u5225\u6709 64 \u500b\u8207 128 \u500b\u9ad8\u65af\u6210\u5206\u7684\u6027\u5225\u8fa8\u8b58\u932f\u8aa4\u7387\u3002\u7531\u7d50\u679c \u53ef\u4ee5\u770b\u51fa\u4f7f\u7528 128 \u500b\u9ad8\u65af\u6210\u5206\u7684\u932f\u8aa4\u7387\u8f03\u4f4e\uff0c\u800c\u4e14\u4e5f\u6709\u8457\u4e0d\u932f\u7684\u8fa8\u8b58\u7387\uff0c\u56e0\u6b64\u5728\u5f8c\u9762\u7684 \u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u4f7f\u7528 128 \u500b\u9ad8\u65af\u6210\u5206\u7684 GMM \u4f86\u9032\u884c\u985e\u795e\u7d93\u7db2\u8def\u7684\u9078\u64c7\u3002 \u8868\u4e94\u6bd4\u8f03\u672c\u6587\u63d0\u51fa\u7684\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u8207 baseline \u8fa8\u8b58\u7cfb\u7d71\u7684\u7cfb\u7d71\u8fa8\u8b58\u6548\u80fd\uff0c\u5728 \u4e09\u500b\u6e2c\u8a66\u5b50\u96c6\u4e2d\u3002\u53ef\u4ee5\u770b\u51fa\u5728\u4e09\u500b\u6e2c\u8a66\u5b50\u96c6\u7684\u90e8\u5206\uff0c\u672c\u6587\u63d0\u51fa\u7684\u8fa8\u8b58\u7cfb\u7d71\uff0c\u8a5e\u932f\u8aa4\u7387\u76f8 \u8f03\u65bc baseline \u90fd\u6709\u660e\u986f\u7684\u4e0b\u964d\uff0c\u5e73\u5747\u7684\u8a5e\u932f\u8aa4\u7387\u5247\u964d\u4f4e\u4e86 5.9% (\u5f9e 5.25 \u5230 4.94)\uff0c\u6211\u5011 \u76f8\u4fe1\u6b64\u8fa8\u8b58\u7d50\u679c\u652f\u6301\u4f9d\u64da\u8072\u5b78\u7d50\u6027\u5207\u5272\u8a13\u7df4\u8a9e\u6599\u5eab\uff0c\u4e26\u5728\u6e2c\u8a66\u4e2d\u9078\u64c7\u8f03\u4f73\u7684\u8072\u5b78\u6a21\u578b\u505a \u70ba\u8f38\u51fa\uff0c\u5373\u80fd\u9069\u7576\u7684\u63d0\u5347\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u7684\u6548\u80fd\u4e26\u5f37\u5065\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u3002 \u4e94\u3001\u7d50\u8ad6 \u5728\u6b64\u7bc7\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u57fa\u65bc EC \u53ca Mixture of Experts \u7684\u67b6\u69cb\u4f86\u8a13\u7df4\u795e\u7d93\u7db2\u8def\uff1b\u4f9d \u64da\u8a13\u7df4\u8a9e\u6599\u4e0d\u540c\u7684\u8072\u5b78\u7279\u6027\uff0c\u5207\u5272\u4e26\u4ee5\u985e\u795e\u7d93\u7db2\u8def\u8207 GMM \u6a21\u578b\u5316\u4e0d\u540c\u7684\u8072\u5b78\u6a21\u578b\uff1b\u5728 \u6e2c\u8a66\u6642\uff0c\u5c07 \u6e2c\u8a66\u8a9e\u6599\u7d93\u7531 GMM-gate \u5f97\u5230\u5c0d\u6bcf\u500b\u8072\u5b78\u6a21\u578b\u7684\u5f8c\u9a57\u6a5f\u7387\uff0c\u9078\u64c7\u6700\u4f73\u7684\u8072\u5b78 \u6a21\u578b\u505a\u70ba\u8fa8\u8b58\u7cfb\u7d71\u7684\u57fa\u790e\u3002\u5be6\u9a57\u4e0a\uff0c\u6211\u5011\u4ee5 Aurora 2 \u505a\u70ba\u5be6\u9a57\u7684\u8a9e\u6599\u5eab\uff0c\u5c07\u8a13\u7df4\u8a9e\u6599\u4f9d \u64da\u6027\u5225\u4ee5\u53ca SNR \u7684\u65b9\u5f0f\u5207\u5272\u8a13\u7df4\u8a9e\u6599\uff0c\u4e26\u6bd4\u8f03\u4e86\u50b3\u7d71\u4f7f\u7528 DNN-HMM \u67b6\u69cb\u8207\u672c\u6587\u63d0\u51fa \u7684\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u3002\u6211\u5011\u63d0\u51fa\u7684\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u80fd\u63d0\u5347\u50b3\u7d71\u7684\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u9054 5.9%\u3002\u672a\u4f86\u6211\u5011\u5c07\u63a2\u8a0e\u4e0d\u540c\u7684\u8072\u5b78\u7279\u6027\u6a21\u578b\u8207\u4e0d\u540c\u7684 gate function\uff0c\u4e26\u5617\u8a66\u5728\u5927\u8a5e\u5f59\u8a9e \u6599\u5eab\u4e2d\u3002 \u8868\u56db\u3001GMM \u6027\u5225\u8fa8\u8b58\u4e4b\u7d50\u679c" |
| } |
| } |
| } |
| } |
