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\u7531\u65bc\u8fd1\u5e74\u4f86\u96fb\u8166\u8a08\u7b97\u80fd\u529b\u7684\u63d0\u6607\u4ee5\u53ca\u8a9e\u97f3\u8fa8\u8b58\u6280\u8853\u7684\u9032\u6b65\uff0c\u8a9e\u97f3\u8655\u7406\u5728\u6211\u5011\u65e5\u5e38\u751f\u6d3b\u4e0a\u7684\u61c9\u7528\u8207\u65e5\u4ff1\u589e\uff0c\u5982\u8a9e \u97f3\u8fa8\u8b58\u3001\u8a9e\u97f3\u5408\u6210\u3001\u8a9e\u8005\u8b58\u5225\u7b49\u7b49\u3002\u5176\u4e2d\uff0c\u5728\u8de8\u570b\u754c\u7684\u8a9e\u8a00\u5b78\u7fd2\u4e2d\uff0c\u4ee5\u96fb\u8166\u8f14\u52a9\u4f7f\u7528\u8005\u9032\u884c\u975e\u6bcd\u8a9e\u5b78\u7fd2(CALL, Segment Duration[10]\u3002 \u81f3\u65bc\u82f1\u6587\u7684\u8a9e\u97f3\u8a55\u5206\uff0c2002 \u5e74\u6e05\u83ef\u5927\u5b78\u7684\u674e\u4fca\u6bc5\u4ee5\u6885\u723e\u5012\u983b\u8b5c\u3001Magnitude \u53ca Pitch \u4e09\u7a2e\u8a55\u5206\u53c3\u6578\u89c0\u5bdf\u5c0d \u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7684\u5f71\u97ff\uff0c\u5176\u5be6\u9a57\u767c\u73fe\u6885\u723e\u5012\u983b\u8b5c\u53c3\u6578\u5c0d\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7684\u91cd\u8981\u6027\u6700\u5927\uff0c\u53e6\u5916\u4ed6\u4e5f\u5c07\u5404\u500b\u7279\u5fb5\u7684\u5dee\u7570 \u7a0b\u5ea6\u8f49\u63db\u6210\u5206\u6578\uff0c\u4ee5\u56de\u994b\u7d66\u4f7f\u7528\u8005\u53c3\u8003[15]\u30022004 \u5e74\u9673\u6c5f\u6751\u548c\u5f35\u667a\u661f\u7b49\u4eba\u5229\u7528\u4e86 HMM \u548c GMM \u5206\u5225\u5c0d\u4e2d\u6587\u7684 \u767c\u97f3\u548c\u8072\u8abf\u9032\u884c\u8a55\u5206\uff0c\u4e26\u4ee5 Downhill Simplex Search \u9032\u884c\u4e86\u8a55\u5206\u7cfb\u7d71\u53c3\u6578\u7684\u6700\u4f73\u5316\uff0c\u4ee5\u6c42\u9054\u5230\u548c\u4e2d\u6587\u5c08\u5bb6\u4e00\u81f4 \u63a5\u4e0b\u4f86\u7684\u8ad6\u8ff0\u4e2d\uff0c\u9996\u5148\u6211\u5011\u63d0\u51fa\u5be6\u4f5c\u300c\u8aaa\u8a71\u9a57\u8b49\u300d\u7684\u65b9\u6cd5\uff0c\u5305\u542b\u8072\u5b78\u6a21\u578b\u76f8\u4f3c\u5ea6\u6392\u540d\u3001\u9a57\u8b49\u7cfb\u7d71\u7684\u5efa\u7acb \u53ca\u9a57\u8b49\u7cfb\u7d71\u7684\u53ef\u9760\u6027\u7b49\u3002\u63a5\u8457\u662f\u300c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u300d\u3002\u9019\u90e8\u4efd\u5305\u542b\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(Hidden Markov Model)\u7684\u8a13 \u7df4\u548c\u4ee5\u7dad\u7279\u6bd4\u6f14\u7b97\u6cd5(Viterbi algorithm)\u70ba\u57fa\u790e\u7684\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u6280\u5de7\u3002\u518d\u4f86\u662f\u300c\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u300d\uff0c\u5176\u4e2d\u63d0\u5230\u4e86\u95dc \u65bc\u8a55\u5206\u53c3\u6578\u7684\u64f7\u53d6\u3001\u8a55\u5206\u53c3\u6578\u6b63\u898f\u5316\u3001\u5716\u6a23\u6bd4\u5c0d\u6d41\u7a0b\u3001\u8a55\u5206\u6a5f\u5236\u7684\u5efa\u7acb\u7b49\uff0c\u4e26\u8a2d\u8a08\u5be6\u9a57\u4ee5\u6c42\u51fa\u5404\u8a55\u5206\u53c3\u6578\u5728\u82f1 \u6587\u8a9e\u97f3\u8a55\u5206\u4e2d\u7684\u6b0a\u91cd\uff0c\u4ee5\u7b26\u5408\u4eba\u985e\u5c08\u5bb6\u5c0d\u82f1\u6587\u8a9e\u53e5\u597d\u58de\u7684\u770b\u6cd5\u3002\u6700\u5f8c\u662f\u7e3d\u7d50\u53ca\u4eca\u5f8c\u7814\u7a76\u5de5\u4f5c\u7684\u5c55\u671b\u3002 3 \u82f1\u8a9e\u8a55\u5206\u7cfb\u7d71\u67b6\u69cb \u5728\u6b64\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u4e2d\uff0c\u9996\u5148\u4ee5\u8aaa\u8a71\u9a57\u8b49\u505a\u70ba\u7b2c\u4e00\u9053\u6aa2\u8996\u95dc\u5361\uff0c\u723e\u5f8c\u4ee5\u8072\u5b78\u6a21\u578b\u4f86\u5c0d\u6a19\u6e96\u8a9e\u97f3\u53ca\u8a55\u5206\u8a9e\u97f3\u5207 \u5272\u97f3\u7d20\u7684\u6642\u9593\u5340\u6bb5\uff0c\u518d\u5c07\u9019\u4e9b\u8cc7\u8a0a\u9001\u81f3\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u7684\u6838\u5fc3\uff0c\u5229\u7528\u5404\u7a2e\u8a55\u5206\u53c3\u6578\uff0c\u9010\u97f3\u7d20\u5730\u6bd4\u8f03\u8a55\u5206\u8a9e\u97f3 \u548c\u6a19\u6e96\u8a9e\u97f3\u7684\u5dee\u7570\u7a0b\u5ea6\u3002\u672c\u6587\u6240\u63d0\u4e4b\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u67b6\u69cb\u6d41\u7a0b\uff0c\u5982\u5716\u8868 1\u6240\u793a\u3002 \u5716\u8868 1 \u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u6d41\u7a0b\u5716 3.1 \u8aaa\u8a71\u9a57\u8a3c \u6240\u8b02\u7684\u8aaa\u8a71\u9a57\u8b49(Utterance Verification)\uff0c\u5c31\u662f\u6211\u5011\u53ef\u4ee5\u91dd\u5c0d\u4e0d\u540c\u7684\u8a55\u5206\u8a9e\u97f3\u7522\u751f\u5224\u65b7\u6578\u503c\uff0c\u4e26\u4f9d\u6b64\u800c\u5c0d\u8a72\u8a55\u5206 \u8a9e\u97f3\u5167\u5bb9\u7684\u6b63\u78ba\u6027\u505a\u51fa\u5224\u65b7[1]\u3002\u6b64\u8aaa\u8a71\u9a57\u8b49\u6d41\u7a0b\u5982\u5716\u8868 2 \u6240\u793a\uff0c\u7576\u9a57\u8b49\u7cfb\u7d71\u63a5\u6536\u5230\u8a9e\u97f3\u8a0a\u865f\u5f8c\uff0c\u5206\u5225\u5c0d\u6bcf\u500b \u97f3\u7d20\u9032\u884c\u8a9e\u97f3\u8fa8\u8b58\uff0c\u4e4b\u5f8c\u518d\u4f9d\u8fa8\u8b58\u7d50\u679c\u7684\u6a5f\u7387\u503c\u6392\u540d\u4e26\u914d\u5408\u9a57\u8b49\u6a5f\u5236\u7d66\u4e88\u6700\u5f8c\u7684\u53ef\u4fe1\u5ea6\u503c\u3002 \u5716\u8868 2 \u8aaa\u8a71\u9a57\u8b49\u7cfb\u7d71\u6d41\u7a0b\u5716 3.1.1 \u97f3\u7d20\u5207\u5272 \u9019\u88e1\u5207\u5272\u7528\u7684\u6280\u8853\uff0c\u4e26\u4e0d\u662f\u7528 Viterbi Decoding \u4e2d\u5e38\u898b\u7684 Forced Alignment\uff0c\u800c\u662f\u4f7f\u7528 beam search \u4e2d pruning \u97f3\u76f8\u7576\u985e\u4f3c\uff0c\u5247\u7d93\u7531\u5207\u5272\u5f8c\u7522\u751f\u97f3\u7d20\u7684\u6578\u91cf\u5c07\u63a5\u8fd1\u751a\u81f3\u7b49\u540c\u65bc\u6a19\u6e96\u8a9e\u97f3\u97f3\u7d20\u7684\u6578\u91cf\u3002\u76f8\u53cd\u5730\uff0c\u82e5\u4e82\u8b1b\u7684\u8a55\u5206\u8a9e \u97f3\u4e2d\u53ea\u6709\u524d n \u500b\u97f3\u7d20\u548c\u6a19\u6e96\u8a9e\u97f3\u76f8\u540c(\u5f8c\u5e7e\u500b\u97f3\u7d20\u5b8c\u5168\u4e0d\u540c)\uff0c\u5247\u7d93\u7531 pruning \u5f8c\u7684\u97f3\u7d20\u4e5f\u5927\u7d04\u7b49\u65bc n\u3002\u8209\u4f8b\u4f86\u8aaa\uff0c \u5982\u679c\u6a19\u6e96\u8a9e\u97f3\u70ba\u300cshe has your dark suit in greasy wash water all year\u300d\u3001\u8a55\u5206\u8a9e\u97f3\u70ba\u300cshe has your dark suit\u300d\uff0c\u5247 \u5c0d\u65bc\u6c92\u6709\u5207\u5272\u51fa\u4f86\u7684\u97f3\u7d20\uff0c\u6211\u5011\u5247\u5c07\u5176\u53ef\u4fe1\u5ea6\u503c\u8a2d\u70ba 0\uff0c\u5982\u6b64\u4e00\u4f86\u53ef\u4ee5\u589e\u52a0\u9a57\u8b49\u7cfb\u7d71\u7684\u5340\u5225\u6027\uff0c\u4f7f\u5f97\u548c\u6a19 \u5716\u8868 3 \u70ba\u5169\u500b\u8a9e\u97f3\u7d93\u7531\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u5f8c\u7522\u751f\u7684\u4e0d\u540c\u7d50\u679c\u3002\u4e0a\u534a\u90e8\u7684\u8a9e\u97f3\u5167\u5bb9\u7b49\u540c\u65bc\u6a19\u6e96\u8a9e\u97f3\u5167\u5bb9\uff0c\u56e0\u6b64 \u7d93\u7531\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u4e4b\u5f8c\uff0c\u7522\u751f\u7684\u7d50\u679c\u53ef\u80fd\u6709\u5169\u7a2e\u60c5\u6cc1\uff1a\u4e00\u7a2e\u662f\u90e8\u4efd\u7684\u8a9e\u97f3\u8a0a\u865f\u5df2\u7d93\u6210\u529f\u5207\u5272\u51fa\u6642\u9593\u5340\u6bb5\u7684\u97f3 \u4efd\u5167\u5bb9\u548c\u6a19\u6e96\u8a9e\u97f3\u5b8c\u5168\u4e0d\u76f8\u540c\u3002\u53e6\u4e00\u90e8\u4efd\u5247\u662f\u8a9e\u97f3\u8a0a\u865f\u5167\u5bb9\u300c\u90e8\u4efd\u76f8\u540c\u300d\u65bc\u6a19\u6e96\u8a9e\u97f3\u5167\u5bb9\u3002\u5728\u6b64\u6211\u5011\u5b9a\u7fa9\u4e00\u53e5 \u6e96\u8a9e\u97f3\u5167\u5bb9\u5b8c\u5168\u4e0d\u76f8\u540c\u7684\u8a55\u5206\u8a9e\u97f3\uff0c\u5176\u53ef\u4fe1\u5ea6\u503c\u8b8a\u5f97\u76f8\u7576\u4f4e\u3002 2. Incorrect\uff1a\u53d6 168 \u53e5\u5167\u5bb9\u4e0d\u7b49\u65bc\u6a19\u6e96\u8a9e\u97f3\u5167\u5bb9\u7684\u8a9e\u6599\uff0c\u9019\u90e8\u4efd\u8a9e\u97f3\u6a94\u6848\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 7 \u5206 31 \u79d2\u3002\u5176\u4e2d\u4e00\u90e8 3.1.3 \u9a57\u8b49\u6a5f\u5236 1. Correct\uff1a \u53d6 168 \u53e5\u8aaa\u8a71\u5167\u5bb9\u76f8\u540c\u7684\u8a9e\u97f3\u8a0a\u865f\u7576\u4f5c\u6a19\u6e96\u8a9e\u97f3\u5167\u5bb9\uff0c\u9019\u90e8\u4efd\u8a9e\u97f3\u6a94\u6848\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 9 \u5206 10 \u79d2\u3002 \u7d93\u7531\u8a9e\u97f3\u8fa8\u8b58\u5f8c\uff0c\u5728\u8a55\u5206\u8a9e\u97f3\u4e2d\u6240\u80fd\u5207\u5272\u51fa\u4f86\u7684\u97f3\u7d20\u6578\u91cf\u662f 15\uff0c\u5982\u5716\u8868 3\u3002 \u540c\u7684\u97f3\u7d20\uff0c\u5373\u4f7f\u6392\u540d\u540c\u6a23\u662f\u7b2c\u4e8c\u540d\uff0c\u53ef\u662f\u548c\u7b2c\u4e00\u540d\u7684\u5c0d\u6578\u6a5f\u7387\u5dee\u8ddd\u537b\u4e0d\u76f8\u540c\uff0c\u6703\u9020\u6210\u9019\u6a23\u7684\u539f\u56e0\u5728\u65bc\u6709\u4e9b\u97f3\u7d20 \u8a55\u5206\u52d5\u4f5c\u3002\u5c0d\u6b64\u6211\u5011\u8490\u96c6\u5169\u90e8\u4efd\u7684\u5be6\u9a57\u8a9e\u6599\uff1a \u4e2d\uff0c\u53ea\u6709\u4e00\u500b model \u7684\u767c\u97f3\u548c\u8a72\u97f3\u7d20\u63a5\u8fd1\uff0c\u56e0\u6b64\u66f4\u52a0\u7a81\u986f\u4e86\u5176\u7b2c\u4e00\u548c\u7b2c\u4e8c\u540d\u7684\u5c0d\u6578\u6a5f\u7387\u5dee\u8ddd\u3002 \u884c\u8a55\u5206\u3002\u76f8\u53cd\u7684\uff0c\u5247\u8868\u793a\u9019\u53e5\u8a71\u548c\u6a19\u6e96\u8a9e\u97f3\u7684\u5167\u5bb9\u4e0d\u76f8\u540c\uff0c\u56e0\u6b64\u6211\u5011\u4e5f\u5c31\u505c\u6b62\u8b93\u5169\u53e5\u4e0d\u76f8\u540c\u7684\u8a9e\u97f3\u9032\u884c\u5f8c\u7e8c\u7684 \u540d phone model \u7684\u767c\u97f3\u5f88\u63a5\u8fd1\uff0c\u9020\u6210\u5c0d\u6578\u6a5f\u7387\u7684\u5dee\u8ddd\u76f8\u7576\u5c0f\u3002 \u800c\u5728\u4e0b\u65b9\u5716\u4e2d\u7684\u97f3\u7d20\uff0c\u4e5f\u8a31\u5728\u6211\u5011 39 \u500b models \u8a0a\u865f\u7684\u5167\u5bb9\u548c\u6a19\u6e96\u8a9e\u97f3\u8a0a\u865f\u7684\u5167\u5bb9\u76f8\u540c\u300d\u9019\u53e5\u8a71\u662f\u76f8\u7576\u53ef\u9760\u7684\uff0c\u4e5f\u5c31\u8868\u793a\u6211\u5011\u53ef\u4ee5\u653e\u5fc3\u5730\u91dd\u5c0d\u9019\u53e5\u8a9e\u97f3\u8a0a\u865f\u9032 \u7684\u767c\u97f3\u76f8\u4f3c\uff0c\u800c\u6709\u4e9b\u97f3\u7d20\u7684\u767c\u97f3\u5dee\u7570\u5247\u76f8\u7576\u5927[16]\uff0c\u56e0\u6b64\u6211\u5011\u5c0d\u65bc\u4e0a\u65b9\u5716\u4e2d\u7684\u97f3\u7d20\uff0c\u53ef\u89e3\u91cb\u6210\u5176\u7b2c\u4e00\u540d\u548c\u7b2c\u4e8c \u5c0d\u65bc\u5728\u5be6\u9a57\u4e2d\u6c42\u51fa\u7684\u9580\u6abb\u503c(threshold)\u800c\u8a00\uff0c\u5982\u679c\u8a9e\u97f3\u8a0a\u865f\u5f97\u5230\u7684\u53ef\u4fe1\u5ea6\u503c\u9ad8\u65bc\u9580\u6abb\u503c\uff0c\u5247\u6211\u5011\u7a31\u300c\u6b64\u53e5\u8a9e\u97f3 \u7684\u65b9\u5f0f\uff0c\u5c07\u8a9e\u97f3\u76e1\u53ef\u80fd\u5730\u4f9d\u5e8f\u5207\u5272\u51fa\u6bcf\u4e00\u500b\u97f3\u7d20\u3002\u5728\u9019\u7a2e\u60c5\u6cc1\u4e0b\uff0c\u8a55\u5206\u8a9e\u97f3\u5207\u5272\u5f8c\uff0c\u5982\u679c\u539f\u4f86\u7684\u5167\u5bb9\u548c\u6a19\u6e96\u8a9e uw\uff0c\u5c31\u7121\u6cd5\u518d\u7e7c\u7e8c\u3002\u5716\u8868 3\u4e4b\u97f3\u7d20\u7b26\u865f\u662f\u63a1\u7528 CMU Phone Set \u8868\u793a\u6cd5[21]\u3002 sil sh iy hh ae d y ao r d aa r k s uw t ah n g r iy s iy w aa sh w ao t er ao l y ih r sil \u22120.2 \u22120.1 0 0.1 0.2 sil sh iy hh ae d y ao r d aa r k s uw \u22120.2 \u22120.1 0 0.1 0.2 \u5716\u8868 3 \u8aaa\u8a71\u9a57\u8b49\u7684\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u6bd4\u8f03\u5716 3.1.2 \u97f3\u7d20\u6392\u540d \u5207\u5272\u8a9e\u97f3\u8a0a\u865f\u5f97\u5230\u97f3\u7d20\u6642\u9593\u5340\u6bb5\u5f8c\uff0c\u9996\u5148\u4ee5\u6bcf\u500b\u97f3\u7d20\u5c0d 39 \u500b phone models \u8a08\u7b97\u5c0d\u6578\u6a5f\u7387[21]\uff0c\u4e26\u4ee5\u6392\u540d\u7684\u9806\u5e8f \u5f97\u5230\u76f8\u5c0d\u61c9\u7684\u53ef\u4fe1\u5ea6\u503c\u3002\u6a5f\u7387\u6392\u540d\u7684\u793a\u610f\u5716\u5982\u5716\u8868 4\uff1a \u5716\u8868 4 \u97f3\u7d20\u6a5f\u7387\u6392\u540d \u4e0a\u4e0b\u5169\u500b\u6a5f\u7387\u5206\u4f48\u8868\u793a\u4e0d\u540c\u7684\u97f3\u7d20\u7d93\u7531\u8fa8\u8b58\u7a0b\u5f0f\u6c42\u5f97 39 \u500b\u5c0d\u6578\u6a5f\u7387\u7684\u7d50\u679c\uff0c\u7531\u5716\u8868 4\u53ef\u4ee5\u770b\u51fa\uff0c\u5c0d\u65bc\u4e0d \u60c5\u6cc1\uff0c\u4e5f\u5c31\u662f\u5982\u4f55\u5c07\u97f3\u7d20\u7684\u6392\u540d\u6b63\u898f\u5316\uff0c\u5f97\u5230\u4e00\u500b\u5408\u7406\u7684\u6578\u503c\u3002 \u5728 Sukkar \u548c ( ) \uf8f7 \uf8f7 \uf8f8 \uf8f6 \uf8ec \uf8ec \uf8ed \uf8eb \u22c5 \u2212 \u22c5 + = 1 log log 1 exp 1 2 Rank Rank pho pho P P Rank value pho \u03b1 ( ) x exp \u8868\u793a x e \uff0c\u5373\u81ea\u7136\u5c0d\u6578\u7684 e \u7684 x \u6b21\u65b9\u3002 pho Rank \u548c pho Rank P log \u5206\u5225\u8868\u793a\u8a72\u97f3\u7d20\u5728 39 \u500b models \u4e2d\u7684\u6392\u540d\u53ca\u5c0d \u6578\u6a5f\u7387\u503c\uff0c1 \u8868\u793a\u7b2c\u4e00\u540d\uff0c\u03b1 \u70ba\u6211\u5011\u8abf\u6574\u7684\u53c3\u6578\u503c\u3002\u7531\u6b64\u516c\u5f0f\u53ef\u5f97\u77e5\uff0c\u7576\u67d0\u97f3\u7d20\u76f8\u5c0d\u65bc 39 \u500b models \u7684\u6392\u540d\u70ba \u7b2c\u4e00\u540d\u6642\uff0c\u8a72\u97f3\u7d20\u7684\u53ef\u4fe1\u5ea6\u503c\u70ba 1\u3002 \u5716\u8868 5 \u8868\u793a\u5c0d\u65bc\u300cSH\u300d\u9019\u500b\u97f3\u7d20\u4e4b\u8a9e\u97f3\u5340\u6bb5\u85c9\u7531\u4e0a\u8ff0\u7684\u516c\u5f0f\u53ef\u5c07\u5176\u5c0d\u61c9\u65bc 39 \u500b models \u6240\u7522\u751f\u7684\u5c0d\u6578\u6a5f \u7387\u53ca\u540d\u6b21\u63db\u7b97\u6210\u53ef\u4fe1\u5ea6\u503c\u3002\u5f9e\u5716\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u7576\u540d\u6b21\u5728\u7b2c 10 \u540d\u5de6\u53f3\u6642\uff0c\u53ef\u4fe1\u5ea6\u503c\u5df2\u7d93\u964d\u81f3 0.2 \u4e86\u3002 \u5716\u8868 5 \u97f3\u7d20 SH \u7684\u6392\u540d\u8207\u53ef\u4fe1\u5ea6\u503c\u7684\u95dc\u4fc2 \u53e6\u5916\u7531\u65bc\u97f3\u7d20\u9593\u767c\u97f3\u7684\u5dee\u7570\u6027\uff0c\u56e0\u6b64\u6211\u5011\u5728\u8a55\u65b7\u53ef\u4fe1\u5ea6\u503c\u6642\uff0c\u4e0d\u80fd\u55ae\u7d14\u5730\u4ee5\u6392\u540d\u4f86\u505a\u6bd4\u8f03\u3002\u8209\u4f8b\u4f86\u8aaa\uff0c\u97f3 \u7d20\u300cOW\u300d\u3014o\u3015\u548c\u300cS\u300d\u3014s\u3015\u6bd4\u5c0d\u5b8c 39 \u500b models \u5f8c\u540c\u6a23\u90fd\u5f97\u5230\u7b2c\u4e8c\u540d\u7684\u7d50\u679c\uff0c\u4f46\u662f\u5c0d\u65bc\u300cOW\u300d\u800c\u8a00\uff0c\u5176\u7b2c \u4e00\u540d\u662f\u300cAO\u300d\u3014R\u3015\uff0c\u800c\u300cS\u300d\u97f3\u7d20\u7684\u7b2c\u4e00\u540d\u662f\u300cT\u300d\u3014t\u3015\uff0c\u5247\u6211\u5011\u53ef\u4ee5\u5f88\u660e\u986f\u5730\u770b\u51fa\u300cOW\u300d\u548c\u7b2c\u4e00\u540d\u7684\u5c0d\u6578 \u6a5f\u7387\u5dee\u8ddd\u8f03\u5c0f\uff0c\u4e5f\u56e0\u6b64\u53ef\u4fe1\u5ea6\u503c\u61c9\u8a72\u8981\u6bd4\u8f03\u9ad8\u624d\u5408\u7406\u3002\u56e0\u6b64\u5728\u4e0a\u8ff0\u516c\u5f0f\u4e2d\uff0c\u6211\u5011\u5c07\u6392\u540d\u7684\u5dee\u7570\u518d\u4e58\u4e0a\u5c0d\u6578\u6a5f\u7387 \u7684\u6bd4\u4f8b\u5dee\u7570\uff0c\u5982\u6b64\u4e00\u4f86\u5c31\u6703\u4f7f\u5f97\u6bcf\u500b\u97f3\u7d20\u7684\u53ef\u4fe1\u5ea6\u503c\u53d7\u5230\u6392\u540d\u53ca\u5c0d\u6578\u6a5f\u7387\u7684\u5f71\u97ff\u3002\u6700\u5f8c\u7d93\u7531\u8a08\u7b97\u5f97\u5230\u7684\u53ef\u4fe1\u5ea6 \u503c\u4ecb\u65bc 0 \u548c 1 \u4e4b\u9593\u3002 \u7576\u8a08\u7b97\u51fa\u53e5\u5b50\u6240\u6709\u6210\u529f\u5207\u5272\u7684\u97f3\u7d20\u53ef\u4fe1\u5ea6\u503c\u4e4b\u5f8c\uff0c\u5229\u7528\u6bcf\u500b\u97f3\u7d20\u7684\u6642\u9593\u9577\u5ea6\u5360\u53e5\u5b50\u6642\u9593\u9577\u5ea6\u7684\u767e\u5206\u6bd4\u4f5c\u70ba \u6b0a\u91cd\uff0c\u5373\u53ef\u63a8\u5c0e\u5f97\u51fa\u4e00\u53e5\u8a9e\u97f3\u8a0a\u865f\u7684\u53ef\u4fe1\u5ea6\u503c\u3002\u4ee5\u4e0b\u662f\u8a2d\u5b9a\u7684\u516c\u5f0f\uff1a ( ) ( ) \u2211 = \u22c5 \u22c5 = N n pho n sen n value sentence len pho len value 1 100 , N \u70ba\u4e00\u55ae\u5b57\u4e2d\u8a55\u5206\u97f3\u7d20\u7684\u6578\u91cf\uff0c ( ) x len \u8868\u793a x \u7684\u6642\u9593\u9577\u5ea6\u3002 \u81f3\u65bc\u6709\u4e9b\u55ae\u5b57\u53ef\u80fd\u5176\u4e2d\u7684\u4e00\u4e9b\u97f3\u7d20\u6c92\u6709\u8fa6\u6cd5\u7d93\u7531\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u7522\u751f\uff0c\u5c0d\u65bc\u9019\u4e9b\u97f3\u7d20\uff0c\u6211\u5011\u5c31\u76f4\u63a5\u5c07\u5176 pho value \u8a2d\u70ba 0\u3002\u6700\u5f8c\u4e58\u4e0a\u5e38\u6578 100 \u4ee3\u8868\u6211\u5011\u5c07\u8aaa\u8a71\u9a57\u8b49\u7cfb\u7d71\u7684\u7d50\u679c\u5b9a\u7fa9\u5728 0 \u81f3 100 \u4e4b\u9593\u3002 3.1.4 \u8aaa\u8a71\u9a57\u8b49\u5be6\u9a57\u7d50\u679c \u5be6\u9a57\u7528\u7684\u8a9e\u6599\u5176\u97f3\u8a0a\u683c\u5f0f\u7686\u70ba PCM\uff0c\u97f3\u8a0a\u53d6\u6a23\u983b\u7387\u70ba 16 kHz\uff0c\u4f4d\u5143\u89e3\u6790\u5ea6\u70ba 16 bits\uff0c\u6240\u6709\u7684\u5be6\u9a57\u8a9e\u6599\u7686 \u70ba\u55ae\u8072\u9053\u3002\u63a5\u8457\u5c07\u4e0a\u8ff0\u5169\u90e8\u4efd\u5404 168 \u53e5\u7684\u5be6\u9a57\u8a9e\u6599\u7d93\u7531\u8aaa\u8a71\u9a57\u8b49\u7cfb\u7d71\u5f97\u5230\u5c0d\u61c9\u7684\u53ef\u4fe1\u5ea6\u503c\uff0c\u800c\u5f8c\u518d\u7d71\u8a08\u3001\u5206\u6790 \u9019\u4e9b\u53ef\u4fe1\u5ea6\u503c\u5373\u6c42\u5f97\u9a57\u8b49\u7cfb\u7d71\u7684\u9580\u6abb\u503c\u3002\u5716\u8868 6 \u70ba\u6c42\u53d6\u9580\u6abb\u503c\u7684\u5be6\u9a57\u7d50\u679c\u5206\u4f48\u5716\uff0c\u6a6b\u8ef8\u70ba\u53ef\u4fe1\u5ea6\u503c\u7684\u7bc4\u570d\uff0c \u7e31\u8ef8\u70ba\u53ef\u4fe1\u5ea6\u503c\u8655\u65bc\u8a72\u7bc4\u570d\u5167\u7684\u8a9e\u97f3\u8a0a\u865f\u500b\u6578\u3002 \u5716\u8868 6 \u8aaa\u8a71\u9a57\u8b49\u6c42\u53d6\u9580\u6abb\u503c\u5be6\u9a57\u7d50\u679c\u5206\u4f48\u60c5\u6cc1 \u6211\u5011\u4ee5\u300c\u578b\u5225 I 3.2 \u8a9e\u97f3\u8a0a\u865f\u5207\u5272 \u300c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u300d\u6a21\u7d44\u7684\u529f\u80fd\u4e43\u662f\u5c07\u6a19\u6e96\u8a9e\u6599\u53ca\u8a55\u5206\u8a9e\u6599\u5207\u5272\u51fa\u97f3\u7d20\u767c\u97f3\u7684\u5340\u6bb5\u3002\u5176\u4f5c\u6cd5\u662f\u4ee5\u9810\u5148\u8a13\u7df4\u597d\u7684\u82f1 \u6587\u767c\u97f3\u8072\u5b78\u6a21\u578b\uff0c\u5207\u5272\u51fa\u8a9e\u6599\u4e2d\u4e4b\u6b63\u78ba\u7684\u97f3\u7d20\u767c\u97f3\u5340\u6bb5\u3002\u4ee5\u4e0b\u7ae0\u7bc0\u5c07\u5206\u6210\u300c\u8072\u5b78\u6a21\u578b\u7684\u8a13\u7df4\u300d\u548c\u300c\u5229\u7528\u8a9e\u97f3\u8fa8 \u8b58\u4f86\u9032\u884c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u300d\u9019\u5169\u90e8\u4efd\u4f86\u4ecb\u7d39\u3002 3.2.1 3.2.2 \u8072\u5b78\u6a21\u578b\u8a2d\u8a08 \u82f1\u6587\u4e2d\u6bcf\u4e00\u500b\u97f3\u7bc0\u53ef\u80fd\u7531\u4e00\u500b\u6216\u6578\u500b\u97f3\u6a19\u6240\u7d44\u6210\uff0c\u800c\u6bcf\u4e00\u500b\u97f3\u6a19\u90fd\u6703\u5c0d\u61c9\u5230\u4e00\u500b\u97f3\u7d20\uff0c\u800c\u8072\u8abf\u3001\u91cd\u97f3\u548c\u7834\u97f3 \u97f3\u6a19 \u6a21\u578b \u97f3\u6a19 \u6a21\u578b \u97f3\u6a19 \u6a21\u578b \u97f3\u6a19 \u6a21\u578b \u97f3\u6a19 (multiple pronunciation)\u6a21\u578b AA</td></tr></table>",
"html": null,
"type_str": "table",
"text": "Computer-Assisted Language Learning)\u5df2\u53d7\u5230\u76f8\u7576\u91cd\u8996\uff0c\u5404\u65b9\u4e5f\u7d1b\u7d1b\u6295\u5165\u76f8\u95dc\u7684\u7814\u7a76[10][11][18][15][20]\u3002 \u96fb\u8166\u8f14\u52a9\u767c\u97f3\u8a13\u7df4(CAPT, Computer-Assisted Pronunciation Training)\u53ef\u8996\u70ba\u662f\u8a9e\u97f3\u8fa8\u8b58\u548c\u5716\u5f62\u6bd4\u5c0d(Pattern Matching)\u5169\u9805\u6280\u8853\u7684\u7d50\u5408\u3002\u672c\u8ad6\u6587\u7814\u7a76\u4e3b\u984c\uff0c\u5305\u542b\u300c\u8aaa\u8a71\u9a57\u8b49\u300d\u3001\u300c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u300d\u4ee5\u53ca\u300c\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u300d\u4e09 \u500b\u90e8\u4efd\uff0c\u5e0c\u671b\u878d\u5408\u76ee\u524d\u8a9e\u97f3\u8fa8\u8b58\u548c\u5716\u5f62\u6bd4\u5c0d\u7684\u6280\u8853\uff0c\u5c0d\u4f7f\u7528\u8005\u9032\u884c\u516c\u6b63\u7684\u8a9e\u97f3\u8a55\u5206\u3002 \u5728\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u4e2d\uff0c\u5982\u679c\u80fd\u5148\u6ffe\u9664\u5167\u5bb9\u548c\u6a19\u6e96\u8a9e\u97f3\u5b8c\u5168\u4e0d\u540c\u7684\u8a55\u5206\u8a9e\u97f3\uff0c\u53ef\u4ee5\u4f7f\u6574\u500b\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u66f4\u5177 \u516c\u4fe1\u529b\u3002\u672c\u8ad6\u6587\u904b\u7528\u4e86\u53ef\u4fe1\u5ea6\u8a55\u4f30\u7684\u6280\u8853\u4f86\u9054\u6210\u8aaa\u8a71\u9a57\u8b49(Utterance Verification)\u3002\u78ba\u4fdd\u4e86\u8a55\u5206\u8a9e\u97f3\u5167\u5bb9\u7684\u6b63\u78ba \u6027\u5f8c\uff0c\u5c0d\u65bc\u8a55\u5206\u8a9e\u97f3\u6211\u5011\u4f7f\u7528 HMM(Hidden Markov Model)\u5207\u5272\u51fa\u6bcf\u500b\u97f3\u7d20(phoneme)\u7684\u6642\u9593\u5340\u6bb5\uff0c\u4f7f\u7528\u9ad8\u8fa8\u8b58 \u7387\u7684 HMM \u8072\u5b78\u6a21\u578b\u53ef\u78ba\u4fdd\u5207\u5272\u51fa\u4f86\u7684\u97f3\u7d20\u5340\u6bb5\u6709\u4e00\u5b9a\u7684\u53ef\u4fe1\u5ea6\u53ca\u6b63\u78ba\u7387\u3002\u5728\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u90e8\u4efd\uff0c\u6211\u5011\u5229\u7528\u6a19 \u6e96\u8a9e\u97f3\u8cc7\u6599\u4f86\u9032\u884c\u4e00\u7a2e\u8f03\u70ba\u4e3b\u89c0\u7684\u8a55\u5206\u65b9\u5f0f\uff0c\u4e3b\u8981\u4f7f\u7528\u5716\u6a23\u6bd4\u5c0d(Pattern Matching)\u7684\u65b9\u6cd5\uff0c\u6839\u64da\u56db\u500b\u8a55\u5206\u53c3\u6578\uff1a \u97f3\u91cf\u5f37\u5ea6\u66f2\u7dda(Magnitude) \u3001\u57fa\u983b\u8ecc\u8de1\u66f2\u7dda(Pitch Contour) \u3001\u767c\u8072\u6025\u7de9\u8b8a\u5316(Rhythm)\u4ee5\u53ca HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570(HMM Log-Likelihood)\uff0c\u5c07\u8a55\u5206\u8a9e\u97f3\u548c\u6a19\u6e96\u8a9e\u97f3\u7684\u8cc7\u6599\u9010\u97f3\u7d20\u5730\u4f86\u505a\u6bd4\u8f03\uff0c\u4ee5\u671f\u627e\u51fa\u8a55\u5206\u8a9e\u97f3\u548c\u6a19\u6e96\u8a9e\u97f3\u7684\u5dee\u7570\u7a0b\u5ea6\u3002 2 \u76f8\u95dc\u7814\u7a76 1997 \u5e74\u6642\uff0cC. Cucchiarini\u3001H. Strik \u53ca L. Boves \u4ee5\u8377\u862d\u8a9e\u70ba\u4e3b\uff0c\u5b9a\u7fa9\u4e86 Total Duration of Speech no/plus Pause\u3001 Mean Segment Duration \u3001 Rate of Speech \u4ee5 \u53ca Global Log-Likelihood \uff0c \u7d93 \u7531 \u985e \u4f3c \u7684 \u5be6 \u9a57 \u5f8c \u5f97 \u51fa Global Log-Likelihood \u5c0d\u65bc\u4eba\u985e\u4e3b\u89c0\u8a55\u5206\u5360\u8f03\u91cd\u7684\u6bd4\u91cd[19]\u30021999 \u5e74 L. Neumeyer\u3001H. Franco\u3001V. Digalakis \u548c M. Weintraub \u4ee5\u6cd5\u8a9e\u8a9e\u6599\u5eab\u9032\u884c\u5be6\u9a57\uff0c\u63a1\u7528 HMM Log-Likelihood\u3001Normalized Acoustic\u3001Segment classification\u3001 Segment Duration\u3001Timing \u7576\u4f5c\u5176\u5be6\u9a57\u7684\u8a55\u5206\u53c3\u6578\uff0c\u7d93\u7531\u5be6\u9a57\u5f8c\u5f97\u51fa\u4e86 Normalized Acoustic \u5728\u8a55\u5206\u7cfb\u7d71\u548c\u8a9e\u8a00 \u5c08\u5bb6\u7d66\u4e88\u7684\u5206\u6578\u4e2d\uff0c\u5176\u76f8\u95dc\u6027\u9ad8\u65bc Lee \u65bc 1996 \u5e74\u767c\u8868\u7684\u8ad6\u6587[17]\u4e2d\u63d0\u5230\uff0c\u97f3\u7d20\u7684\u5c0d\u6578\u6a5f\u7387\u4ee5\u53ca\u5c0d\u6240\u6709\u97f3\u7d20\u7684\u5c0d\u6578\u6a5f\u7387\u6392\u540d\uff0c\u548c \u9a57\u8b49\u7cfb\u7d71\u7684\u53ef\u4fe1\u5ea6\u503c\u662f\u6709\u5f88\u5927\u5f71\u97ff\u7684\u3002\u57fa\u65bc\u4ee5\u4e0a\u7684\u524d\u63d0\uff0c\u6211\u5011\u5c07 Sukkar \u548c Lee \u6240\u63d0\u51fa\u6c42\u53d6\u53ef\u4fe1\u5ea6\u503c\u7684\u5f0f\u5b50\u6539\u5beb \u4e26\u4ee5\u4e0b\u5217\u516c\u5f0f\u8868\u793a\uff1a \u932f\u8aa4\u7387(Type I error, False Reject)\u52a0\u4e0a\u578b\u5225 II \u932f\u8aa4\u7387(Type II error, False Accept)\u70ba\u6700\u5c0f\u300d\u4f5c \u70ba\u5c0b\u627e\u9580\u6abb\u503c\u7684\u524d\u63d0\u3002\u6839\u64da\u5be6\u9a57\u7d50\u679c\uff0c\u6211\u5011\u767c\u73fe Correct \u4e2d\u7684\u8a9e\u6599\u5176\u6700\u5c0f\u53ef\u4fe1\u5ea6\u503c\u70ba 63.21\uff0c\u800c\u5728 Incorrect \u53ef\u4fe1 \u5ea6\u503c\u5927\u65bc 60 \u7684\u8a9e\u6599\u4e2d\u6700\u63a5\u8fd1 63.21 \u7684\u53ef\u4fe1\u5ea6\u503c\u70ba 61.59\uff0c\u56e0\u6b64\u6211\u5011\u5c07\u8aaa\u8a71\u9a57\u8b49\u7cfb\u7d71\u7684\u9580\u6abb\u503c\u8a2d\u5b9a\u6210 62.40(\u5373\u5169 \u8005\u7684\u5e73\u5747)\uff0c\u5982\u6b64\u53ef\u9054\u5230\u578b\u5225 I \u932f\u8aa4\u7387\u70ba 0%\uff0c\u578b\u5225 II \u932f\u8aa4\u7387\u70ba\u70ba 1.19%\u3002 \u7d93\u7531\u4e0a\u8ff0\u5be6\u9a57\u8a08\u7b97\u6c42\u51fa\u9580\u6abb\u503c\u5f8c\uff0c\u6211\u5011\u53e6\u5916\u6e96\u5099\u4e00\u7d44\u5167\u542b Correct \u53ca Incorrect \u5404\u70ba 168 \u53e5\u7684\u6e2c\u8a66\u8a9e\u6599\uff0c\u5176 \u4e2d Correct \u8a9e\u6599\u7684\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 7 \u5206 27 \u79d2\uff0cIncorrect \u8a9e\u6599\u7684\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 8 \u5206 57 \u79d2\u3002\u5c07\u9019\u4e9b\u8a9e\u6599\u4ee5\u9580 \u6abb\u503c\u70ba 62.40 \u7684\u5be6\u9a57\u7d50\u679c\uff0c\u5176\u578b\u5225 I \u932f\u8aa4\u7387\u70ba 7.14%\uff0c\u578b\u5225 II \u932f\u8aa4\u7387\u70ba\u70ba 0.60%\u3002 \u8072\u5b78\u6a21\u578b HMM \u7684\u8a9e\u6599 \u5be6\u4f5c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u4e4b\u524d\uff0c\u6211\u5011\u5fc5\u9808\u5148\u7522\u751f\u8072\u5b78\u6a21\u578b\uff0c\u624d\u80fd\u91dd\u5c0d\u5404\u7a2e\u4e0d\u540c\u7684\u8a9e\u97f3\u9032\u884c\u5207\u5272\u52d5\u4f5c\u3002\u672c\u8ad6\u6587\u4e2d\u6211\u5011\u8a2d \u8a08\u4e86\u5169\u7a2e\u4e0d\u540c\u7684\u8072\u5b78\u6a21\u578b\uff1a\u4e00\u500b\u662f\u81fa\u7063\u4eba\u53e3\u97f3\u7684\u8072\u5b78\u6a21\u578b\uff0c\u4e00\u500b\u662f\u5916\u570b\u4eba\u6a19\u6e96\u8a9e\u97f3\u7684\u8072\u5b78\u6a21\u578b\u3002 \u9996\u5148\u91dd\u5c0d\u6bcd\u8a9e\u70ba\u82f1\u6587\u7684\u8072\u5b78\u6a21\u578b\uff0c\u6211\u5011\u4f7f\u7528 TIMIT \u8a9e\u6599\u4f86\u52a0\u4ee5\u8a13\u7df4\u3002\u8a9e\u6599\u5167\u5bb9\u70ba 2,342 \u53e5\u5e73\u8861\u8a9e\u6599\uff0c\u7531 438 \u4f4d\u7537\u6027\u3001192 \u4f4d\u5973\u6027\uff0c\u5171 630 \u4eba\u9304\u88fd\uff0c\u6bcf\u4eba\u5206\u914d\u9304\u88fd 10 \u53e5\uff0c\u6545\u5171\u6709 6,300 \u53e5\u8a9e\u97f3\u3002\u4f9d TIMIT \u7684\u5efa\u8b70\u53d6\u5176\u4e2d 4,620 \u53e5\u3001\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 3 \u5c0f\u6642 49 \u5206 10 \u79d2\u7684\u8a9e\u97f3\u8a0a\u865f\u4f5c\u70ba\u6bcd\u8a9e\u70ba\u82f1\u6587\u7684\u8072\u5b78\u6a21\u578b\u8a13\u7df4\uff0c\u53e6\u5916 1,680 \u53e5\u3001\u8a9e \u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 1 \u5c0f\u6642 23 \u5206 51 \u79d2\u7684\u8a9e\u97f3\uff0c\u5247\u4f5c\u70ba\u5916\u5728\u6e2c\u8a66\u6a94(Outside Test)\u3002 \u53e6\u4e00\u65b9\u9762\u91dd\u5c0d\u6bcd\u8a9e\u70ba\u570b\u8a9e\u7684\u8072\u5b78\u6a21\u578b\uff0c\u6211\u5011\u8acb 33 \u4f4d\u5b78\u751f\uff0c\u5176\u4e2d\u5305\u542b\u4e86 23 \u4f4d\u7537\u6027\u300110 \u4f4d\u5973\u6027\uff0c\u4f9d TIMIT \u7684\u8cc7\u6599\u9304\u88fd 7,026 \u53e5\u5e73\u8861\u8a9e\u6599\uff0c\u6211\u5011\u53d6\u5176\u4e2d\u7684 4,684 \u53e5\u3001\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 4 \u5c0f\u6642 11 \u5206 3 \u79d2\u7684\u8a9e\u97f3\u4f5c\u70ba\u6bcd\u8a9e\u70ba \u4e2d\u6587\u7684\u8072\u5b78\u6a21\u578b\u8a13\u7df4\uff0c\u800c\u53e6\u5916\u7684 2,342 \u53e5\u3001\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 1 \u5c0f\u6642 57 \u5206 43 \u79d2\u7684\u8a9e\u97f3\u4f5c\u70ba\u5916\u5728\u6e2c\u8a66\u6a94\u3002 \u4e0a\u8ff0\u8a9e\u6599\u7684\u97f3\u8a0a\u683c\u5f0f\u7686\u70ba PCM\uff0c\u53d6\u6a23\u983b\u7387\u70ba 16 kHz\uff0c\u4f4d\u5143\u89e3\u6790\u5ea6\u70ba 16 bits\u3002 \u7684\u554f\u984c\uff0c\u5728\u76ee\u524d\u7684\u8072\u5b78\u6a21\u578b\u8a2d\u8a08\u4e2d\u5247\u66ab\u6642\u5ffd\u7565\u3002TIMIT \u7684\u5b57\u5178\u6709 62 \u500b\u97f3\u7d20\uff0c\u7531\u65bc\u83ef\u4eba \u5c0d\u65bc\u4e00\u4e9b\u97f3\u7d20\u4e0d\u50cf\u5916\u570b\u4eba\u5ff5\u5f97\u90a3\u9ebc\u6e96\u78ba\uff0c\u518d\u52a0\u4e0a\u8a13\u7df4\u8a9e\u6599\u4e0d\u8db3\u4e0b\uff0c\u5982\u679c\u6211\u5011\u6e1b\u5c11\u8a13\u7df4 model \u7684\u500b\u6578\uff0c\u5247\u53ef\u4f7f\u6bcf \u500b model \u7684\u8a13\u7df4\u8a9e\u6599\u53d6\u6a23\u6578\u76ee\u589e\u591a\u3002\u9451\u65bc\u4e0a\u8ff0\u5169\u500b\u539f\u56e0\uff0c\u6211\u5011\u5c07\u539f\u5148 TIMIT \u8a2d\u8a08\u7684 62 \u500b\u97f3\u7d20\u522a\u6e1b\u6210 40 \u500b\u97f3\u7d20 (\u542b\u975c\u97f3 SIL \u97f3\u7d20)\u3002\u5728\u672c\u7ae0\u4e2d\u6211\u5011\u4f7f\u7528\u7684\u8072\u5b78\u6a21\u578b\u548c\u97f3\u7d20\u662f\u4e00\u5c0d\u4e00\u5c0d\u61c9\u7684\u3002\u8209\u4f8b\u4f86\u8aaa\uff0c\"school\"\u9019\u500b\u55ae\u5b57\uff0c\u5176 KK \u97f3\u6a19\u70ba\u3014skul\u3015\uff0c\u4ee5\u6211\u5011\u8a2d\u8a08\u7684\u8072\u5b78\u6a21\u578b\u4f86\u8aaa\uff0c\u5c31\u662f\u300cS\u300d\uff0b\u300cK\u300d\uff0b\u300cUW\u300d\uff0b\u300cL\u300d\u3002\u8868\u683c 1 \u662f\u6211\u5011\u6240 \u8a2d\u8a08\u7684 40 \u500b\u8072\u5b78\u6a21\u578b\u8207 KK \u97f3\u6a19\u5c0d\u7167\u8868\uff1a \u8868\u683c 1 40 \u500b\u8072\u5b78\u6a21\u578b\u8207 KK \u97f3\u6a19\u5c0d\u7167\u8868",
"num": null
},
"TABREF1": {
"content": "<table><tr><td>Hidden Markov \u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u7684\u4e3b\u8981\u76ee\u6a19\u5373\u662f\u5e0c\u671b\u80fd\u5920\u5c07\u9023\u7e8c\u7684\u82f1\u6587\u8a9e\u97f3\u53e5\u5b50\uff0c\u5176\u4e2d\u5305\u542b\u4e86\u6a19\u6e96\u8a9e\u97f3\u548c\u8a55\u5206\u7684\u8a9e\u97f3\uff0c\u5207\u5272\u6210\u7368 Model Toolkit)\u9032\u884c\u8a13\u7df4\u3002 3.2.4 \u8a13\u7df4\u7d50\u679c \u7acb\u7684\u97f3\u7d20\uff0c\u5982\u6b64\u4e00\u4f86\u6211\u5011\u624d\u53ef\u4ee5\u91dd\u5c0d\u6bcf\u4e00\u6bb5\u53e5\u5b50\u4e2d\u7684\u97f3\u7d20\u548c\u6a19\u6e96\u8a9e\u97f3\u4e2d\u7684\u6bcf\u4e00\u500b\u97f3\u7d20\u505a\u6bd4\u8f03\u3002\u5728\u6b64\u6211\u5011\u4f7f\u7528\u5f37 \u8feb\u5c0d\u61c9(Forced Alignment)[6]\u7684\u65b9\u5f0f\u5c07\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u6210\u5404\u500b\u97f3\u7d20\u7684\u6642\u9593\u5340\u6bb5\uff0c\u4ee5\u5229\u8a55\u5206\u6a5f\u5236\u7684\u904b\u4f5c\u3002\u5728\u524d\u8655\u7406\u7684 \u904e\u7a0b\u4e2d\uff0c\u6211\u5011\u5229\u7528\u5167\u542b 127,102 \u500b\u82f1\u6587\u55ae\u5b57\u7684 CMU \u5b57\u5178(Dictionary from Carnegie Mellon University)\u5c0d\u5404\u55ae\u5b57\u6a19 \u97f3\u4e26\u5efa\u7acb\u5404\u81ea\u7368\u7acb\u7684\u8fa8\u8b58\u7db2\u8def[21]\u3002\u5982\u4e0b\u5716\uff1a \u5716\u8868 7 \u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u524d\u8655\u7406\u6d41\u7a0b\u793a\u610f\u5716 \u5b8c\u6210\u524d\u8655\u7406\u52d5\u4f5c\u5f8c\uff0c\u6211\u5011\u53ef\u7e7c\u7e8c\u9032\u884c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u7684\u6d41\u7a0b\uff0c\u9996\u5148\u5c07\u4e00\u8a9e\u97f3\u8a0a\u865f\u7d93\u904e\u7aef\u9ede\u5075\u6e2c\u5f8c\u518d\u7d93\u7531\u7279 \u5fb5\u64f7\u53d6\uff0c\u53d6\u51fa\u8a9e\u97f3\u4e2d\u7684\u7279\u5fb5\uff0c\u7136\u5f8c\u5c07\u9019\u4e9b\u7279\u5fb5\u53c3\u6578\u900f\u904e\u8072\u5b78\u6a21\u578b(\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b)\u53ca\u8a9e\u8a00\u6a21\u578b(\u8fa8\u8b58\u7db2\u8def)\uff0c\u5229 \u7528\u7dad\u7279\u6bd4\u6f14\u7b97\u6cd5(Viterbi algorithm)\u5373\u53ef\u627e\u51fa\u6700\u76f8\u4f3c\u7684\u97f3\u7d20\uff0c\u4e26\u5f97\u77e5\u5404\u97f3\u7d20\u7684\u6642\u9593\u5340\u6bb5\u3002 \u8868\u683c 2 \u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u5be6\u9a57\u7d50\u679c \u5be6\u9a57\u65b9\u5f0f \u9805\u76ee N-Wave /N-HMM N-Wave /T-HMM T-Wave /N-HMM T-Wave /T-HMM \u5be6\u9a57\u8a9e\u6599\u97f3\u7d20\u7e3d\u6578 58,282 58,282 81,229 81,229 \u5207\u5272\u5f8c\u6b63\u78ba\u97f3\u7d20\u7e3d\u6578 58,253 57,142 77,293 80,230 \u97f3\u7d20\u6642\u9593\u6b63\u78ba\u7387 99.95% 98.04% 95.15% 98.77% \u5728\u5224\u65b7\u97f3\u7d20\u6642\u9593\u6b63\u78ba\u7387\u7684\u90e8\u4efd\uff0c\u5c0d\u65bc N-Wave \u800c\u8a00\uff0c\u7531\u65bc\u6240\u6709\u7684\u8a9e\u6599 TIMIT \u90fd\u6709\u63d0\u4f9b\u6a19\u97f3\u6a94\uff0c\u56e0\u6b64\u6211\u5011 \u53ef\u6bd4\u5c0d\u5207\u5272\u51fa\u4f86\u7684\u6642\u9593\u9ede\u548c\u6a19\u97f3\u6a94\uff0c\u82e5\u76f8\u5dee\u5728 0.1 \u79d2\u4ee5\u5167(5 \u500b\u97f3\u6846)\uff0c\u5247\u6211\u5011\u7a31\u6b64\u97f3\u7d20\u7684\u6642\u9593\u70ba\u6b63\u78ba\u3002\u800c\u5c0d\u65bc T-Wave \u800c\u8a00\uff0c\u7531\u65bc\u4e26\u6c92\u6709\u7d93\u904e\u4eba\u5de5\u6a19\u97f3\uff0c\u56e0\u6b64\u6211\u5011\u53ea\u5728\u9f90\u5927\u7684\u8a9e\u6599\u4e2d\u53d6\u6a23 10%\u9032\u884c\u4eba\u5de5\u5224\u65b7\uff0c\u53ea\u8981\u8a72\u5340\u6bb5\u4eba \u8033\u807d\u8d77\u4f86\u76f8\u5dee\u4e0d\u5927\uff0c\u5247\u6211\u5011\u7a31\u8a72\u97f3\u7d20\u7684\u6642\u9593\u70ba\u6b63\u78ba\u3002 \u7531\u8868\u683c 2 \u7684\u5be6\u9a57\u7d50\u679c\u53ef\u77e5\uff0c\u5728\u4e0d\u540c\u7684\u8072\u5b78\u6a21\u578b\u4e0b\uff0cForced Alignment \u7684\u97f3\u7d20\u6642\u9593\u5340\u6bb5\u90fd\u975e\u5e38\u6e96\u78ba\u3002\u8868\u683c 3 \u5247\u662f N-Wave\u3001T-Wave \u900f\u904e\u5927\u8a5e\u5f59\u8fa8\u8b58\u7684\u65b9\u5f0f\uff0c\u7d93\u7531 N-HMM\u3001T-HMM \u6240\u5f97\u51fa\u7684\u8fa8\u8b58\u7387\uff0c\u5176\u4e2d\u8a5e\u5f59\u5167\u5bb9\u70ba 2,342 \u53e5\u82f1\u6587\u53e5\u5b50\u3002 \u5be6\u9a57\u8a9e\u6599\u53e5\u5b50\u7e3d\u6578 1,680 1,680 2,342 2,342 \u8fa8\u8b58\u6b63\u78ba\u53e5\u5b50\u7e3d\u6578 1,650 622 1,997 1,425 \u53e5\u5b50\u8fa8\u8b58\u7387 98.21% 37.02% 85.26% 60.85% \u7531\u8868\u4e2d\u7684\u7d50\u679c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\uff0c\u5c0d\u65bc\u76f8\u540c\u8a9e\u6599\uff0cN-HMM \u7684\u8fa8\u8b58\u7387\u7686\u9ad8\u65bc T-HMM\uff0c\u9019\u5c31\u8868\u793a\u7576\u6211\u5011\u4ee5 N-HMM \u70ba\u8072\u5b78\u6a21\u578b\u4f86\u5c0d\u8a9e\u97f3\u8a0a\u865f\u6c42\u53d6\u5c0d\u6578\u6a5f\u7387\u6642\uff0c\u6240\u5f97\u5230\u7684\u5c0d\u6578\u6a5f\u7387\u503c\u5176\u53ef\u4fe1\u5ea6\u6703\u9ad8\u65bc T-HMM\u3002\u6839\u64da\u6b64\u5be6\u9a57\u7d50\u679c\uff0c \u5728\u63a5\u4e0b\u4f86\u7684\u7ae0\u7bc0\u4e2d\uff0c\u6211\u5011\u5c07\u6703\u4ee5 N-HMM \u7576\u4f5c\u6211\u5011\u8a55\u5206\u6bd4\u5c0d\u7684\u8072\u5b78\u6a21\u578b\u3002 3.3 \u82f1\u6587\u8a9e\u97f3\u8a55\u5206 \u5716\u8868 8 \u70ba\u8a55\u5206\u7cfb\u7d71\u6d41\u7a0b\u5716\uff0c\u6211\u5011\u5c07\u5c31\u8a55\u5206\u53c3\u6578\u64f7\u53d6\u3001\u5716\u6a23\u6bd4\u5c0d\u65b9\u5f0f\u548c\u8a55\u5206\u6a5f\u5236\u5efa\u7acb\u5206\u5225\u4f5c\u4ecb\u7d39\u3002 3.3.1 \u8a55\u5206\u53c3\u6578\u64f7\u53d6 \u9664\u4e86\u97f3\u91cf\u5f37\u5ea6\u66f2\u7dda\u3001\u57fa\u983b\u8ecc\u8de1\u66f2\u7dda\u70ba\u8a55\u5206\u53c3\u6578\u5916[15]\uff0c\u6211\u5011\u4e5f\u63a1\u7528\u4e86 HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u548c\u767c\u8072\u6025\u7de9\u8b8a\u5316\u9019\u5169 \u9805\u8a55\u5206\u53c3\u6578\u3002\u5728 Forced Alignment \u7684\u540c\u6642\uff0c\u6211\u5011\u53ef\u4ee5\u5f97\u5230\u6bcf\u500b\u97f3\u7d20\u5c0d\u61c9\u65bc\u8072\u5b78\u6a21\u578b\u7684\u5c0d\u6578\u6a5f\u7387(HMM log-Probability)[10][11]\u548c\u5404\u97f3\u7d20\u7684\u6642\u9593\u5340\u6bb5\uff0c\u9019\u5c31\u662f\u6240\u8b02\u7684 HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u548c\u767c\u8072\u6025\u7de9\u8b8a\u5316\u9019\u5169\u9805\u8a55\u5206\u53c3 \u6578\u3002 3.3.2 \u5716\u6a23\u6bd4\u5c0d\u65b9\u6cd5 \u5728\u524d\u4e09\u500b\u8a55\u5206\u53c3\u6578\u4e2d\uff0c\u6211\u5011\u4f7f\u7528\u4e0d\u540c\u7684\u6b63\u898f\u5316\u65b9\u6cd5\u5982\u5167\u63d2\u6cd5\u3001\u7dda\u6027\u5e73\u79fb\u548c\u7dda\u6027\u7e2e\u653e[15]\uff0c\u5982\u8868\u683c 4\u3002\u800c HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u5247\u63a1\u7528\u8f03\u70ba\u4e0d\u540c\u7684\u6bd4\u5c0d\u65b9\u6cd5\uff0c\u5728\u4ee5\u4e0b\u8aaa\u660e\u3002 \u8868\u683c 4 \u5404\u8a55\u5206\u53c3\u6578\u63a1\u7528\u7684\u6b63\u898f\u5316\u53ca\u8ddd\u96e2\u7b97\u6cd5 \u8a55\u5206\u53c3\u6578 \u6b63\u898f\u5316\u65b9\u6cd5 \u8ddd\u96e2\u7b97\u6cd5 \u97f3\u91cf\u5f37\u5ea6\u66f2\u7dda \u5167\u63d2\u6cd5\u3001\u7dda\u6027\u7e2e\u653e Euclidean Distance \u57fa\u983b\u8ecc\u8de1\u66f2\u7dda \u5167\u63d2\u6cd5\u3001\u7dda\u6027\u5e73\u79fb Euclidean Distance \u767c\u8072\u6025\u7de9\u8b8a\u5316 \u7121 Euclidean Distance \u5716\u8868 9 HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u6bd4\u5c0d\u6d41\u7a0b\u5716 \u7531\u65bc\u6a5f\u7387\u503c\u662f\u7d55\u5c0d\u7684\uff0c\u4e0d\u5bb9\u6613\u5f9e\u6578\u503c\u76f4\u63a5\u4f5c\u6bd4\u8f03\uff0c\u56e0\u6b64\u6211\u5011\u8a2d\u8a08\u4e86\u6a5f\u7387\u500d\u6578\u4f86\u4fee\u6b63\u5c0d\u6578\u6a5f\u7387\u7684\u5dee\u7570\u503c\uff0c\u7576 \u5169\u8a9e\u97f3\u7684\u5c0d\u6578\u6a5f\u7387\u7d55\u5c0d\u503c\u7686\u5c0f\u65bc 1050 \u6642\uff0c\u6a5f\u7387\u500d\u6578\u7684\u8b8a\u5316\u8da8\u52e2\u8f03\u5c0f\u3002\u7576\u5169\u8a9e\u97f3\u7684\u5c0d\u6578\u6a5f\u7387\u7d55\u5c0d\u503c\u7686\u5927\u65bc 1050 \u6642\uff0c\u6a5f\u7387\u500d\u6578\u7684\u8b8a\u5316\u8da8\u52e2\u8f03\u5927\u3002\u95dc\u65bc\u6a5f\u7387\u500d\u6578\u6211\u5011\u5b9a\u7fa9\u4ee5\u4e0b\u7684\u516c\u5f0f\uff1a log log 1050 , 1400 log , 1 min 3 1050 0 , 350 log abs y probabilit abs y probabilit Const \uf8f4 \uf8f4 \uf8f3 \uf8f4 \uf8f4 \uf8f2 \uf8f1 > \uf8f7 \uf8f7 \uf8f8 \uf8f6 \uf8ec \uf8ec \uf8ed \uf8eb \uf8fa \uf8fa \uf8f9 \uf8ef 4 3 2 1 4 3 2 1 , , , , , , , b b b b a a a a \u548c\u56db\u500b\u8a55\u5206\u53c3\u6578\u7684\u6b0a\u91cd 4 3 2 1 , , , w w w \u3002 w \uf8ef \uf8ee \u2212 + \u2264 \u2264 \uf8fa \uf8fa \uf8f9 \uf8ef \uf8ef \uf8ee \u2212 = \u8868\u683c 5 \u4eba\u5de5\u8a55\u5206\u548c\u7cfb\u7d71\u8a9e\u97f3\u8a55\u5206\u7684\u95dc\u4fc2\u5c0d\u7167\u8868 3.3.3 \u8a55\u5206\u6a5f\u5236\u5efa\u7acb \u5728\u97f3\u7d20\u5c64\u6b21\uff0c\u6211\u5011\u7531\u56db\u7a2e\u8a55\u5206\u53c3\u6578\u5f97\u5230\u4e0d\u540c\u7684\u5206\u6578\uff0c\u518d\u5f80\u4e0a\u7531\u55ae\u5b57(word)\u548c\u53e5\u5b50(sentence)\u5c64\u6b21\u4f5c\u8a55\u5206\uff0c\u5c31\u53ef\u4ee5 \u5f97\u5230\u6700\u5f8c\u8a55\u5206\u7684\u7d50\u679c\uff0c\u4ee5\u4e0b\u5247\u5206\u56db\u500b\u5c64\u6b21\u4f5c\u4ecb\u7d39\u3002 \u8a55\u5206\u53c3\u6578\u5c64\u6b21\uff1a\u5c0d\u65bc\u6bcf\u500b\u97f3\u7d20\u4e2d\u8a55\u5206\u53c3\u6578\u7684\u5206\u6578\uff0c\u6211\u5011\u8a2d\u5b9a\u4ee5\u4e0b\u7684\u516c\u5f0f[15]\uff1a ( ) b fea dist a score \u22c5 + = 1 100 \u7531\u9019\u500b\u516c\u5f0f\u6211\u5011\u5c31\u53ef\u4ee5\u5c07\u5169\u97f3\u7d20\u9593\u67d0\u500b\u7279\u5fb5\u7684\u5dee\u7570\u7a0b\u5ea6\u8f49\u6210 0 \u5230 100 \u4e4b\u9593\u7684\u5206\u6578\uff0c\u53ea\u8981\u8a2d\u5b9a\u597d\u5169\u7d44\u7684 dist \u53ca\u5c0d \u61c9\u7684 fea score \uff0c\u5373\u53ef\u5f9e\u4e2d\u6c42\u51fa a \u548c b \uff0c\u63a5\u8457\u6240\u6709\u7684\u8ddd\u96e2\u4e5f\u5c07\u53ef\u4ee5\u8a08\u7b97\u51fa\u5c0d\u61c9\u7684\u5206\u6578\u3002 \u97f3\u7d20\u5c64\u6b21\uff1a\u7576\u8a08\u7b97\u51fa\u6bcf\u500b\u97f3\u7d20\u4e2d\u56db\u9805\u8a55\u5206\u53c3\u6578\u7684\u5206\u6578\u5f8c\uff0c\u5229\u7528\u56db\u9805\u7279\u5fb5\u5c0d\u65bc\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u7cfb\u7d71\u6240\u5360\u7684\u6b0a \u91cd\u52a0\u7e3d\u5f8c\u5373\u53ef\u5f97\u5230\u6bcf\u500b\u97f3\u7d20\u7684\u5206\u6578\u3002\u4ee5\u4e0b\u662f\u8a2d\u5b9a\u7684\u516c\u5f0f\uff1a 4 3 2 1 4 3 2 1 fea fea fea fea pho score w score w score w score w score \u22c5 + \u22c5 + \u22c5 + \u22c5 = \uff0c 4 3 2 1 w w w w \u3001 \u3001 \u3001 \u5206\u5225\u4ee3\u8868\u56db\u500b\u8a55\u5206\u53c3\u6578\u7684\u6b0a\u91cd\u3002\u7d93\u7531\u4e0b\u4e00\u7bc0\u7684\u5be6\u9a57\uff0c\u6211\u5011\u53ef\u4ee5\u6c42\u51fa\u9019\u56db\u9805\u6b0a\u91cd\uff0c\u4e5f\u53ef\u4ee5\u7531\u6b0a\u91cd\u7684 \u6bd4\u4f8b\u5f97\u77e5\u56db\u9805\u8a55\u5206\u53c3\u6578\u5c0d\u65bc\u82f1\u6587\u8a55\u5206\u7684\u91cd\u8981\u6027\u3002 \u55ae\u5b57\u5c64\u6b21\uff1a\u5f97\u77e5\u6bcf\u500b\u97f3\u7d20\u7684\u5f97\u5206\u5f8c\uff0c\u4ee5\u6bcf\u500b\u97f3\u7d20\u5360\u55ae\u5b57\u7684\u6642\u9593\u70ba\u6b0a\u91cd\uff0c\u5373\u53ef\u6c42\u51fa\u53e5\u5b50\u4e2d\u6bcf\u4e00\u500b\u55ae\u5b57\u7684\u5206 \u53e5\u5b50\u5c64\u6b21\uff1a\u7531\u65bc\u55ae\u5b57\u7684\u6642\u9593\u9577\u77ed\u6703\u5f71\u97ff\u4eba\u8033\u5c0d\u65bc\u4e00\u53e5\u8a71\u7684\u95dc\u6ce8\u9ede\uff0c\u56e0\u6b64\u6211\u5011\u4e5f\u662f\u4ee5\u55ae\u5b57\u7684\u6642\u9593\u70ba\u6b0a\u91cd\u4f86 \u8a08\u7b97\u51fa\u4e00\u53e5\u8a9e\u97f3\u8a0a\u865f\u6700\u5f8c\u5f97\u5230\u7684\u5206\u6578\u3002\u4ee5\u4e0b\u70ba\u5b9a\u7fa9\u7684\u516c\u5f0f\uff1a ( ) ( ) \u2211 = \u22c5 = N n word n sen n score sentence len word len score 1 \uff0c\u5176\u4e2d N \u8868\u793a\u53e5\u5b50\u4e2d\u55ae\u5b57\u7684\u7e3d\u6578\uff0clen(x)\u8868\u793a x \u7684\u6642\u9593\u9577\u5ea6\u3002 4 \u5be6\u9a57\u7d50\u679c \u5f97\u5230\u56db\u500b\u8a55\u5206\u53c3\u6578\u4e2d\u5404\u97f3\u7d20\u7684\u5dee\u7570\u7a0b\u5ea6\u5f8c\uff0c\u6211\u5011\u4f9d\u6240\u4f54\u7684\u6bd4\u4f8b\u6c42\u51fa\u4e00\u500b\u53e5\u5b50\u7684\u5e73\u5747\u5dee\u7570\u7a0b\u5ea6\uff0c\u5373\u53ef\u4ee3\u5165\u4ee5\u4e0b\u7684 \u516c\u5f0f\uff1a ( ) ( ) ( ) ( ) 4 3 2 1 4 4 4 3 3 3 2 2 2 1 1 1 1 100 1 100 1 100 1 100 b b b b dist a w dist a w dist a w dist a w score \u22c5 + \u22c5 + \u22c5 + \u22c5 + \u22c5 + \u22c5 + \u22c5 + \u22c5 = \u5176\u4e2d 4 3 2 1 4 3 2 1 , , , , , , , b b b b a a a a \u70ba\u5dee\u7570\u7a0b\u5ea6\u8f49\u6210\u5206\u6578\u7684\u53c3\u6578\uff0c 4 3 2 1 , , , w w w w \u70ba\u56db\u500b\u8a55\u5206\u53c3\u6578\u7684\u6b0a\u91cd\uff0c\u800c , , 2 1 dist dist , 3 dist 4 dist \u8868\u793a\u6a19\u6e96\u8a9e\u97f3\u548c\u8a55\u5206\u8a9e\u97f3\u8a0a\u865f\u5728\u6bd4\u5c0d\u5f8c\u5176\u56db\u9805\u8a55\u5206\u53c3\u6578\u7684\u8ddd\u96e2\uff0c\u518d\u7d93\u7531\u4ee5\u4e0b\u7684\u5be6\u9a57\uff0c\u5373\u53ef\u6c42\u5f97\u5404\u53c3 \u6578\u503c\u3002 \u5728\u8a9e\u6599\u8a13\u7df4\u90e8\u4efd\u6211\u5011\u6536\u96c6 200 \u7d44\u8a9e\u6599\uff0c\u6bcf\u4e00\u7d44\u7684\u8a9e\u6599\u5206\u5225\u5305\u62ec\u4e00\u53e5\u6a19\u6e96\u8a9e\u97f3\u548c\u4e00\u53e5\u8a55\u5206\u8a9e\u97f3\uff0c\u6bcf\u53e5\u8a9e\u97f3 \u9577\u5ea6\u70ba 5 200 \u7d44\u8a9e\u53e5\u4f5c\u70ba\u6e2c\u8a66\u7528\u3002 \u5c07 \u9019 200 \u7d44 \u8a13 \u7df4 \u8a9e \u6599 \u900f \u904e \u8a55 \u5206 \u7cfb \u7d71 \u8a55 \u5206 \uff0c \u5247 \u6bcf \u7d44 \u8a55 \u5206 \u8a9e \u97f3 \u90fd \u6703 \u5f97 \u5230 \u56db \u500b \u7279 \u5fb5 \u5c0d \u61c9 \u7684 \u5dee \u7570 \u7a0b \u5ea6 4 3 2 1 , , , dist dist dist dist \u3002\u6536\u96c6\u4e86\u9019\u4e9b\u5dee\u7570\u7a0b\u5ea6\u548c\u5c0d\u61c9\u7684\u5206\u6578\u5f8c\uff0c\u4f7f\u7528 Simplex Downhill Search\uff0c\u5c31\u53ef\u4ee5\u627e\u51fa \u4eba\u5de5\u8a55\u5206 \u7cfb\u7d71\u8a55\u5206 Bad Average Good Bad 28 17 7 Average 20 27 20 Good 10 11 63 \u5176\u4e2d\u6a6b\u8ef8\u8868\u793a\u4eba\u5de5\u8a55\u5206\u7684\u7b49\u7d1a\u9805\u76ee\uff0c\u7e31\u8ef8\u8868\u793a\u7cfb\u7d71\u8a55\u5206\u7684\u7b49\u7d1a\u9805\u76ee\uff0c\u8868\u683c\u4e2d\u7684\u6578\u5b57\u5247\u8868\u793a\u76f8\u5c0d\u7684\u8a9e\u53e5\u6578 \u76ee\u3002\u5f9e\u8868\u4e2d\u6211\u5011\u53ef\u4ee5\u660e\u986f\u5730\u770b\u51fa\u4f86\uff0c\u5c0d\u89d2\u7dda\u7684\u6578\u76ee\u90fd\u6bd4\u540c\u4e00\u5217\u3001\u540c\u4e00\u6b04\u7684\u6578\u76ee\u9ad8\uff0c\u9019\u5c31\u8868\u793a\u5728\u7d93\u7531 Simplex Downhill Search \u8abf\u6574\u5404\u53c3\u6578\u4e4b\u5f8c\uff0c\u6211\u5011\u7684\u8a55\u5206\u7cfb\u7d71\u548c\u4eba\u5de5\u8a55\u5206\u5df2\u6709\u4e00\u5b9a\u7684\u6b63\u76f8\u95dc\u6027\uff0c\u7d04 (28+27+63) / 200 = 59%\u3002 5 \u7d50\u8ad6 \u300c\u8aaa\u8a71\u9a57\u8b49\u300d\u5c0d\u8a55\u5206\u8a9e\u97f3\u9032\u884c\u521d\u6b65\u7684\u8a55\u4f30\uff0c\u82e5\u53ef\u4fe1\u5ea6\u5920\u9ad8\uff0c\u63a5\u4e0b\u4f86\u7684\u8a55\u5206\u624d\u5177\u6709\u53ef\u4fe1\u5ea6\u3002\u300c\u8a9e\u97f3\u8a0a\u865f\u5207\u5272\u300d\u5247 \u662f\u4ee5 Forced Alignment \u5f97\u5230\u6bcf\u500b\u97f3\u7d20\u7684\u6642\u9593\u5340\u6bb5\u3002\u7d93\u7531\u5be6\u9a57\u7d50\u679c\u6211\u5011\u53ef\u4ee5\u77e5\u9053\uff0c\u4f7f\u7528\u8fa8\u8b58\u7387\u8f03\u9ad8\u7684\u8072\u5b78\u6a21\u578b\uff0c \u5176 Forced Alignment \u7684\u97f3\u7d20\u5207\u5272\u6642\u9593\u5c07\u66f4\u70ba\u6e96\u78ba\u3002\u300c\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u300d\u5305\u62ec\u8a55\u5206\u53c3\u6578\u7684\u64f7\u53d6\u3001\u5716\u6a23\u6bd4\u5c0d\u65b9\u6cd5\u7684\u8a2d \u8a08\u548c\u8a55\u5206\u6a5f\u5236\u7684\u5efa\u7acb\u7b49\u4e09\u500b\u90e8\u4efd\u3002\u85c9\u7531\u5be6\u9a57\u6211\u5011\u53ef\u4ee5\u77e5\u9053\uff0c\u300cHMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u300d\u5728\u82f1\u6587\u8a9e\u97f3\u8a55\u5206\u4e2d\u6240\u4ee3\u8868 \u7684\u91cd\u8981\u6027\u6700\u9ad8\uff0c\u800c\u300c\u97f3\u91cf\u5f37\u5ea6\u66f2\u7dda\u300d\u5247\u662f\u6700\u4f4e\u3002 \u95dc\u65bc\u5be6\u9a57\u6e2c\u8a66\u8a9e\u6599\u7684\u90e8\u4efd\uff0c\u6211\u5011\u4f7f\u7528\u4e86 1,680 \u53e5\u6bcd\u8a9e\u70ba\u82f1\u6587\u7684\u8a9e\u97f3\u6a94\u6848\uff0c\u5176\u8a9e\u6599\u7684\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 1 \u53c9\u5be6\u9a57\u3002\u8868\u683c 2 \u5217\u51fa\u97f3\u7d20\u5207\u5272\u6b63\u78ba\u7387\u7684\u5be6\u9a57\u7d50\u679c\uff1a \u8868\u683c 3 \u82f1\u6587\u8a9e\u97f3\u8fa8\u8b58\u7387 \u5be6\u9a57\u65b9\u5f0f \u9805\u76ee N-Wave /N-HMM N-Wave /T-HMM T-Wave /N-HMM T-Wave /T-HMM \u70ba\u4e86\u8a08\u7b97 HMM \u5c0d\u6578\u6a5f\u7387\u7684\u5dee\u7570\uff0c\u6211\u5011\u5148\u4ee5 N-HMM(HMM trained from Native Speaker)\u6c42\u51fa\u6a19\u6e96\u8a9e\u97f3\u8a0a\u865f \u53ca\u8a55\u5206\u8a9e\u97f3\u8a0a\u865f\u4e2d\u6bcf\u500b\u97f3\u7d20\u7684\u5c0d\u6578\u6a5f\u7387\uff0c\u82e5\u5c0d\u6578\u6a5f\u7387\u503c\u6108\u5927\uff0c\u8868\u793a\u8a72\u97f3\u7d20\u7684\u767c\u97f3\u6108\u63a5\u8fd1\u8072\u5b78\u6a21\u578b\u3002\u5716\u8868 9 \u70ba HMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u6bd4\u5c0d\u7684\u6d41\u7a0b\u5716\uff1a \u6578\uff0c\u4ee5\u4e0b\u70ba\u8a2d\u5b9a\u7684\u516c\u5f0f\uff1a ( \u8a9e\u97f3\u8a55\u5206\u7684\u904b\u7528\u76f8\u7576\u5ee3\u6cdb\u4e14\u5be6\u7528\uff0c\u914d\u5408\u672a\u4f86\u6280\u8853\u7684\u6210\u719f\uff0c\u4e0d\u53ea\u53ef\u4f5c\u70ba\u82f1\u8a9e\u5b78\u7fd2\u7684\u5de5\u5177\uff0c\u4e4b\u5f8c\u7684\u53f0\u8a9e\u3001\u5ba2 ) ( ) \u2211 = \u22c5 = N n pho n word n score word len pho len \u8a9e\u8a55\u5206\u5b78\u7fd2\u4e5f\u5c07\u662f\u53f0\u7063\u5730\u5340\u91cd\u8981\u7684\u7814\u7a76\u4e4b\u4e00\u3002 score 1 \uff0c\u5176\u4e2d N \u70ba\u4e00\u55ae\u5b57\u4e2d\u8a55\u5206\u97f3\u7d20\u7684\u6578\u91cf\uff0clen(x)\u8868\u793a x \u7684\u6642\u9593\u9577\u5ea6\u3002 \u53c3\u8003\u8cc7\u6599</td></tr><tr><td>2 \u7d93\u7531\u4e0a\u8ff0\u7684\u5be6\u9a57\uff0c\u6211\u5011\u5f97\u5230\u97f3\u91cf\u5f37\u5ea6\u66f2\u7dda\u7684\u6b0a\u91cd\u70ba 7.45%\uff0c\u57fa\u983b\u8ecc\u8de1\u66f2\u7dda\u7684\u6b0a\u91cd\u70ba 22.40%\uff0c\u767c\u8072\u6025\u7de9\u8b8a\u5316 2 ) ( ) ( Evaul stard P Const Const factor + = \u7684\u6b0a\u91cd\u70ba 17.24%\uff0cHMM \u5c0d\u6578\u6a5f\u7387\u5dee\u7570\u7684\u6b0a\u91cd\u70ba 52.91%\u3002</td></tr><tr><td>\u7576\u7b97\u51fa\u6a19\u6e96\u8a9e\u97f3\u548c\u8a55\u5206\u8a9e\u97f3\u7684 Const \u503c\u5f8c\uff0c\u518d\u7d93\u7531\u5e73\u65b9\u76f8\u52a0\u5373\u53ef\u5f97\u5230\u6a5f\u7387\u500d\u6578 \u63a5\u8457\u6211\u5011\u5c07 200 \u53e5\u6e2c\u8a66\u8a9e\u53e5\u7684\u4eba\u5de5\u8a55\u5206\u7d50\u679c\u5206\u6210\u4e09\u500b\u7b49\u7d1a\uff1aBad(0~59)\u3001Average(60~79)\u3001Good(80~100)\uff0c P factor \uff0c\u5c07\u6b64\u6a5f\u7387\u500d\u6578\u4e58 \u53e6\u5916\u4e5f\u628a\u5c07 200 \u53e5\u6e2c\u8a66\u8a9e\u53e5\u7684\u7cfb\u7d71\u8a55\u5206\u7d50\u679c\u4f9d\u6b64\u5206\u6210\u4e09\u500b\u7b49\u7d1a\u3002\u6700\u5f8c\u518d\u7d71\u8a08\u6bcf\u500b\u53e5\u5b50\u7684\u4eba\u5de5\u8a55\u5206\u548c\u7cfb\u7d71\u8a55\u5206 \u4e0a\u5169\u8a9e\u97f3\u8a0a\u865f\u5c0d\u6578\u6a5f\u7387\u7684\u5dee\u8ddd\u5c31\u662f\u6211\u5011\u767c\u97f3\u7279\u5fb5\u7684\u5dee\u7570\u7a0b\u5ea6\u3002 \u5f8c\uff0c\u5c31\u53ef\u4ee5\u5f97\u5230\u8868\u683c 5\u7684\u7d50\u679c\uff1a</td></tr><tr><td>\u5716\u8868 8 \u8a55\u5206\u7cfb\u7d71\u6d41\u7a0b\u5716</td></tr></table>",
"html": null,
"type_str": "table",
"text": "\u5c0f\u6642 23 \u5206 51 \u79d2\uff0c\u4ee5\u4e0b\u6211\u5011\u7c21\u7a31\u70ba N-Wave (Waves from Native-Speaker)\u3002\u53e6\u5916\u4f7f\u7528\u4e86 2,342 \u53e5\u6bcd\u8a9e\u70ba\u570b\u8a9e\u7684\u8a9e\u97f3\u6a94 \u6848\uff0c\u8a9e\u6599\u7684\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 1 \u5c0f\u6642 57 \u5206 43 \u79d2\uff0c\u4ee5\u4e0b\u7c21\u7a31\u70ba T-Wave(Waves from Taiwanese)\uff0c\u4f86\u505a Outside Test\u3002 \u5be6\u9a57\u7528\u7684\u8a9e\u6599\u5176\u97f3\u8a0a\u683c\u5f0f\u7686\u70ba PCM\uff0c\u97f3\u8a0a\u53d6\u6a23\u983b\u7387\u70ba 16 kHz\uff0c\u4f4d\u5143\u89e3\u6790\u5ea6\u70ba 16 bits\u3002 \u5728\u8072\u5b78\u6a21\u578b\u9019\u500b\u90e8\u4efd\uff0c\u6211\u5011\u8a13\u7df4\u51fa\u4e86\u5169\u500b\u8072\u5b78\u6a21\u578b\uff1a\u4e00\u500b\u662f\u7531\u4ee5\u82f1\u6587\u4f5c\u70ba\u6bcd\u8a9e\u7684\u4f7f\u7528\u8005\u6240\u9304\u88fd\u7684\u8a13\u7df4\u8a9e\u6599 \u7522\u751f\u7684\u8072\u5b78\u6a21\u578b\uff0c\u4ee5\u4e0b\u6211\u5011\u7c21\u7a31\u70ba N-HMM(HMM trained from Native-Speaker)\uff0c\u53e6\u4e00\u500b\u5247\u662f\u7531\u81fa\u7063\u4eba\u6240\u9304\u88fd\u7684 \u8a13\u7df4\u8a9e\u6599\u6240\u7522\u751f\u7684\uff0c\u4ee5\u4e0b\u6211\u5011\u7c21\u7a31\u70ba T-HMM(HMM trained from Taiwanese)\u3002 \u95dc\u65bc\u5be6\u9a57\u7684\u65b9\u5f0f\uff0c\u6211\u5011\u5206\u5225\u5c0d\u6bcf\u4e00\u53e5\u8a9e\u97f3\u8a0a\u865f\u548c\u5df2\u77e5\u7684\u8a9e\u97f3\u5167\u5bb9\u6587\u5b57\u4f5c Forced Alignment\uff0c\u518d\u7531\u7522\u751f\u7684\u7d50 \u679c\u5c0d\u6bcf\u500b\u55ae\u5b57\u53ca\u97f3\u7d20\u5224\u65b7\u5176\u6642\u9593\u5340\u6bb5\u7684\u5207\u5272\u662f\u5426\u6b63\u78ba\u3002 \u70ba\u4e86\u6bd4\u8f03\u5169\u500b\u8072\u5b78\u6a21\u578b\u6240\u7522\u751f\u7684\u5f71\u97ff\uff0c\u6211\u5011\u5c0d\u8a9e\u6599(N-Wave, T-Wave) \u548c\u8072\u5b78\u6a21\u578b(N-HMM, T-HMM)\u4f5c\u4ea4 \u79d2\u3001\u97f3\u8a0a\u683c\u5f0f\u70ba PCM\u3001\u97f3\u8a0a\u53d6\u6a23\u983b\u7387\u70ba 16 kHz\u3001\u4f4d\u5143\u89e3\u6790\u5ea6\u70ba 16 bits\u3002\u5176\u4e2d\u6a19\u6e96\u8a9e\u97f3\u7684\u8a9e\u6599\u9577\u5ea6\u7e3d \u548c\u7d04\u70ba 12 \u5206 51 \u79d2\uff0c\u8a55\u5206\u8a9e\u97f3\u7684\u8a9e\u6599\u9577\u5ea6\u7e3d\u548c\u7d04\u70ba 18 \u5206 39 \u79d2\u3002\u63a5\u8457\u8acb\u5916\u8a9e\u6240\u8001\u5e2b\u5354\u52a9\u6211\u5011\u5c0d\u6bcf\u4e00\u53e5\u8a55\u5206\u8a9e\u97f3 \u4f5c\u4e3b\u89c0\u7684\u8a55\u5206\uff0c\u4e4b\u5f8c\u518d\u7d71\u8a08\u5be6\u9a57\u4e2d\u6bcf\u4e00\u53e5\u8a9e\u97f3\u4eba\u70ba\u8a55\u5206\u7684\u5e73\u5747\u5206\u6578\u3002\u540c\u6a23\u7684\uff0c\u6309\u7167\u8a13\u7df4\u8a9e\u53e5\u7684\u4f5c\u6cd5\uff0c\u6211\u5011\u4e5f\u6536 \u96c6\u4e86",
"num": null
}
}
}
} |