{ "paper_id": "O14-1007", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:04:45.501392Z" }, "title": "\u57fa\u65bc\u767c\u97f3\u77e5\u8b58\u4ee5\u5efa\u69cb\u983b\u8b5c HMM \u4e4b\u570b\u8a9e\u8a9e\u97f3\u5408\u6210\u65b9\u6cd5 A Mandarin Speech Synthesis Method Using Articulation-knowledge Based Spectral HMM Structure", "authors": [ { "first": "", "middle": [], "last": "\u53e4\u9d3b\u708e", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "\u3001\u8cf4\u540d\u5f65", "middle": [ "*" ], "last": "\u3001\u6d2a\u5c09\u7fd4", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "", "middle": [], "last": "\u3001\u9673\u5f65\u6a3a", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "Hung-Yan", "middle": [], "last": "Gu", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "Ming-Yen", "middle": [], "last": "Lai", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "Wei-Siang", "middle": [], "last": "Hong", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" }, { "first": "Yan-Hua", "middle": [], "last": "Chen", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science and Technology", "location": {} }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "In this paper, a new HMM structure is proposed to work with a limited training corpus in order to obtain improved synthetic-speech fluency. Spectral fluency is improved because this HMM structure can model the context-dependent spectral characteristics of a speech unit. In addition, instead of using a decision tree to cluster contexts, the knowledge of phoneme articulation is based to cluster contexts and reduce the enormous quantity of context combinations. To evaluate the proposed HMM structure, we construct three Mandarin speech synthesis systems each uses one different HMM structure for comparisons. In these systems, the prosodic parameters are all generated with same ANN modules studied previously", "pdf_parse": { "paper_id": "O14-1007", "_pdf_hash": "", "abstract": [ { "text": "In this paper, a new HMM structure is proposed to work with a limited training corpus in order to obtain improved synthetic-speech fluency. Spectral fluency is improved because this HMM structure can model the context-dependent spectral characteristics of a speech unit. In addition, instead of using a decision tree to cluster contexts, the knowledge of phoneme articulation is based to cluster contexts and reduce the enormous quantity of context combinations. To evaluate the proposed HMM structure, we construct three Mandarin speech synthesis systems each uses one different HMM structure for comparisons. In these systems, the prosodic parameters are all generated with same ANN modules studied previously", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "\u8fd1\u5e74\u4f86\u8a31\u591a\u7814\u7a76\u8005\u65e9\u5df2\u5229\u7528\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(hidden Markov model, HMM)\uff0c\u4f86\u5efa\u9020\u8a9e\u97f3 \u55ae\u5143(\u5982\u97f3\u7d20\u3001\u97f3\u7bc0\u7b49)\u4e4b\u983b\u8b5c\u6f14\u9032(spectrum progression)\u6a21\u578b (Yoshimura et al., 1999; Zen et al., 2007; Yan et al., 2009; Gu et al., 2010) \uff0c\u4e4b\u5f8c\u5728\u5408\u6210\u4e00\u500b\u8a9e\u53e5\u6642\uff0c\u5c31\u6703\u4f7f\u7528\u8a13\u7df4\u5f97\u5230\u7684 HMM \u6a21\u578b\u4f86\u7522\u751f\u4e00\u5e8f\u5217\u7684\u983b\u8b5c\u7279\u5fb5\u5411\u91cf\uff0c\u7136\u5f8c\u4f7f\u7528\u6240\u7522\u751f\u7684\u983b\u8b5c\u7279\u5fb5\u5411\u91cf\u5e8f\u5217\u53bb\u5408\u6210 \u51fa \u8a9e \u97f3 \u4fe1 \u865f \u3002 \u4f7f \u7528 HMM \u4f86 \u4f5c \u8a9e \u97f3 \u4fe1 \u865f \u7684 \u5408 \u6210 \uff0c \u901a \u5e38 \u80fd \u5920 \u7372 \u5f97 \u589e \u9032 \u7684 \u53ef \u7406 \u89e3 \u6027 (intelligibility)\u8207\u6d41\u66a2\u6027(fluency)\u3002\u66f4\u597d\u7684\u662f\uff0cTokuda \u7b49\u4eba\u57fa\u65bc HTK (HMM tool kits)\u6240\u767c \u5c55\u7684 HTS \u8a9e\u97f3\u5408\u6210\u8edf\u9ad4 (Zen et al., 2007) \uff0c\u63d0\u4f9b\u516c\u958b\u7684\u7a0b\u5f0f\u539f\u59cb\u78bc\u3001\u4e26\u4e14\u53ef\u4f9b\u4e0b\u8f09\uff0c\u6240\u4ee5 \u7814\u7a76\u8a9e\u97f3\u5408\u6210\u6642\uff0c\u4f7f\u7528 HTS \u80fd\u5920\u6e1b\u5c11\u8a31\u591a\u6642\u9593\u8207\u6c23\u529b\u3002\u4e0d\u904e\uff0c\u7576\u672a\u4f7f\u7528\u5168\u57df\u8b8a\u7570\u6578(global variance, GV)\u5339\u914d\u6642\uff0cHTS \u8edf\u9ad4\u6240\u7522\u751f\u7684\u983b\u8b5c\u5305\u7d61\u6703\u767c\u751f\u904e\u65bc\u5e73\u6ed1\u7684\u73fe\u8c61\uff0c\u4f7f\u5f97\u5408\u6210\u51fa \u7684\u8a9e\u97f3\u8b8a\u5f97\u60b6\u60b6\u7684(muffled) (Toda & Tokuda, 2005 )\u3002 \u5728\u672c\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u4e26\u4e0d\u6253\u7b97\u6cbf\u7528\u3001\u4fee\u6539 HTS \u7684\u7a0b\u5f0f\u78bc\uff0c\u56e0\u6b64\u5fc5\u9808\u81ea\u884c\u767c\u5c55 HMM \u6a21 \u578b\u8a13\u7df4\u7684\u7a0b\u5f0f\u3001\u53ca\u983b\u8b5c\u7279\u5fb5\u5411\u91cf\u5e8f\u5217\u4e4b\u7522\u751f\u7a0b\u5f0f\uff0c\u9019\u6a23\u7684\u6c7a\u5b9a\u662f\u56e0\u70ba\uff0c\u6211\u5011\u60f3\u8981\u7814\u767c\u4e00 \u500b\u5177\u6709\u5f48\u6027(flexibility)\u7684\u8a9e\u97f3\u5408\u6210\u7cfb\u7d71\uff0c\u80fd\u5920\u5bb9\u6613\u5730\u64f4\u589e\u984d\u5916\u7684\u529f\u80fd\u3002\u4f8b\u5982\u97f3\u8272\u8f49\u63db (timbre transformation)\u4e4b\u529f\u80fd\uff0c\u80fd\u5920\u628a\u5408\u6210\u8a9e\u97f3\u7684\u97f3\u8272\u5f9e\u6210\u5e74\u5973\u6027\u8f49\u8b8a\u6210\u7537\u5b69 (Gu & Tsai, 2013 )\uff1b\u53e6\u4e00\u9805\u9810\u8a08\u64f4\u589e\u7684\u529f\u80fd\u5247\u662f\uff0c\u540c\u6b65\u5730\u64ad\u653e\u5408\u6210\u51fa\u7684\u8a9e\u97f3\u4fe1\u865f\u53ca\u5176\u5c0d\u61c9\u7684\u62fc\u97f3\u7b26 \u865f\uff0c\u6b64\u529f\u80fd\u53ef\u61c9\u7528\u65bc\u4eba\u5f62\u6a5f\u5668\u4eba\u4e0a\uff0c\u8b93\u8a9e\u97f3\u767c\u8072\u548c\u5634\u578b\u540c\u6b65\u3002\u9664\u4e86\u64f4\u589e\u984d\u5916\u529f\u80fd\u7684\u539f\u56e0 \u4e4b\u5916\uff0c\u6211\u5011\u8207\u5176\u4ed6\u7814\u7a76\u8005\u4f7f\u7528 HTS \u7684\u7d93\u9a57 (Hsia et al., 2010) \uff0c\u767c\u73fe HTS \u6240\u7522\u751f\u7684\u570b\u8a9e\u8a9e \u97f3\u7684\u57fa\u9031\u8ecc\u8de1(pitch contours)\u807d\u89ba\u4e0a\u4e26\u4e0d\u4ee4\u4eba\u6eff\u610f\uff0c\u56e0\u6b64\u672c\u8ad6\u6587\u5efa\u9020\u7684\u570b\u8a9e\u8a9e\u97f3\u5408\u6210\u7cfb \u7d71\uff0c\u5c31\u6c7a\u5b9a\u4f7f\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\u4f86\u7522\u751f\u5404\u500b\u97f3\u7bc0\u7684\u57fa\u9031\u8ecc\u8de1\u3002 \u5728\u5148\u524d\u7684\u4e00\u6b21\u7814\u7a76\u4e2d (Gu et al., 2010) \uff0c\u6211\u5011\u66fe\u5617\u8a66\u53bb\u5efa\u7acb\u97f3\u7bc0\u55ae\u4f4d\u7684 HMM \u6a21\u578b\uff0c \u4ee5\u638c\u63e1\u97f3\u7bc0\u5167\u7684\u983b\u8b5c\u6f14\u9032(spectrum progression)\u65b9\u5f0f\uff0c\u4f46\u662f\u6b64 HMM \u6a21\u578b\u6240\u5408\u6210\u51fa\u7684\u8a9e\u97f3 \u4fe1\u865f\u4e26\u4e0d\u5920\u6d41\u66a2\uff0c\u5728\u97f3\u7bc0\u908a\u754c\u8655\u7684\u983b\u8b5c\u4e0d\u9023\u7e8c(spectral discontinuities)\u60c5\u5f62\u7d93\u5e38\u6703\u88ab\u807d\u51fa \u4f86\uff0c\u6211\u5011\u8a8d\u70ba\u767c\u751f\u983b\u8b5c\u4e0d\u9023\u7e8c\u7684\u539f\u56e0\u662f\uff0c\u8a2d\u5b9a\u7684\u8a9e\u97f3\u55ae\u4f4d\"\u97f3\u7bc0\"\u592a\u5927\uff0c\u4f7f\u5f97\u4e00\u500b\u97f3\u7bc0 X \u548c\u524d\u5f8c\u97f3\u7bc0\u6240\u7d44\u5408\u51fa\u7684\u4e0d\u540c\u6587\u8108\u6578\u91cf\u975e\u5e38\u9f90\u5927\uff0c\u4ee5\u81f3\u65bc\u7121\u6cd5\u70ba\u8a72\u97f3\u7bc0 X \u5efa\u7acb\u826f\u597d\u7684\u6587\u8108 \u76f8\u4f9d HMM \u6a21\u578b\u3002\u56e0\u6b64\uff0c\u672c\u8ad6\u6587\u6c7a\u5b9a\u4f7f\u7528\u8f03\u5c0f\u7684\u8a9e\u97f3\u55ae\u4f4d\uff0c\u5373\u8072\u6bcd\u548c\u97fb\u6bcd\uff0c\u63a5\u8457\u6839\u64da\u6a19 \u8a18\u6a94\u88e1\u8a18\u8f09\u7684\u62fc\u97f3\u7b26\u865f\u5e8f\u5217\uff0c\u53bb\u5efa\u69cb\u6587\u8108\u76f8\u4f9d\u7684 HMM \u6a21\u578b\uff0c\u4e26\u4e14\u4f9d\u64da\u767c\u97f3\u77e5\u8b58\u4f86\u5c0d HMM \u6a21\u578b\u4f5c\u5206\u7d44\u8a13\u7df4\u3002HMM \u7d50\u69cb\u7684\u8a2d\u8a08\u65b9\u5f0f\u5c07\u6703\u5728\u7b2c\u4e8c\u7bc0\u8a73\u7d30\u8aaa\u660e\u3002 \u6211\u5011\u5efa\u9020\u7684\u570b\u8a9e\u8a9e\u97f3\u5408\u6210\u7cfb\u7d71\uff0c\u5176\u5408\u6210\u968e\u6bb5\u4e4b\u8655\u7406\u6d41\u7a0b\u5982\u5716 1 \u6240\u756b\uff0c\u5340\u584a(a)\u9032\u884c\u8f38 \u5165\u6587\u53e5\u4e4b\u5206\u6790\uff1b\u5340\u584a(b)\u4f7f\u7528\u985e\u795e\u7d93\u7db2\u8def(Artificial Neural Network, ANN)\u6a21\u7d44\u4f86\u7522\u751f\u5404\u500b \u97f3\u7bc0\u7684\u57fa\u9031\u8ecc\u8de1\u53c3\u6578\u548c\u6642\u9577(duration)\u503c\uff0c\u8a73\u7d30\u4f5c\u6cd5\u53ef\u53c3\u8003 (Gu & Wu, 2009 )\uff1b\u5340\u584a(c)\u4f9d\u64da \u7b2c\u4e8c\u7bc0\u4ecb\u7d39\u7684\u6311\u9078\u65b9\u6cd5\u4f86\u70ba\u5404\u500b\u8072\u97fb\u6bcd\u9078\u51fa\u5c0d\u61c9\u7684 HMM \u6a21\u578b\uff1b\u5340\u584a(d)\u63a1\u7528 Tokuda \u7b49 \u4eba\u63d0\u51fa\u7684\u65b9\u6cd5 (Yoshimura et al., 1999) \uff0c\u53bb\u8a08\u7b97 HMM \u5404\u72c0\u614b\u5206\u914d\u5230\u7684\u6642\u9577\u97f3\u6846\u6578\uff1b\u5340\u584a (e)\u63a1\u7528\u5148\u524d\u7814\u7a76\u63d0\u51fa\u7684\u52a0\u6b0a\u7dda\u6027\u5167\u63d2\u6cd5 (Gu et al., 2010) ", "cite_spans": [ { "start": 94, "end": 118, "text": "(Yoshimura et al., 1999;", "ref_id": "BIBREF10" }, { "start": 119, "end": 136, "text": "Zen et al., 2007;", "ref_id": "BIBREF11" }, { "start": 137, "end": 154, "text": "Yan et al., 2009;", "ref_id": "BIBREF9" }, { "start": 155, "end": 171, "text": "Gu et al., 2010)", "ref_id": "BIBREF3" }, { "start": 384, "end": 402, "text": "(Zen et al., 2007)", "ref_id": "BIBREF11" }, { "start": 538, "end": 558, "text": "(Toda & Tokuda, 2005", "ref_id": "BIBREF7" }, { "start": 737, "end": 753, "text": "(Gu & Tsai, 2013", "ref_id": "BIBREF4" }, { "start": 852, "end": 871, "text": "(Hsia et al., 2010)", "ref_id": "BIBREF5" }, { "start": 972, "end": 989, "text": "(Gu et al., 2010)", "ref_id": "BIBREF3" }, { "start": 1452, "end": 1466, "text": "(Gu & Wu, 2009", "ref_id": "BIBREF2" }, { "start": 1531, "end": 1555, "text": "(Yoshimura et al., 1999)", "ref_id": "BIBREF10" }, { "start": 1601, "end": 1618, "text": "(Gu et al., 2010)", "ref_id": "BIBREF3" } ], "ref_spans": [], "eq_spans": [], "section": "\u7dd2\u8ad6", "sec_num": "1." }, { "text": "\u985e\u4f3c\u65bc\u524d\u4e00\u6bb5\u7684\u6558\u8ff0\uff0c\u5982\u679c\u76ee\u524d\u97f3\u7bc0\u6c92\u6709\u8072\u6bcd\u6642\uff0c\u5247\u5728\u6b64\u97f3\u7bc0\u97fb\u6bcd\u7684\u5de6\u908a\uff0c\u5c07\u6703\u9047 \u5230\u524d\u4e00\u97f3\u7bc0\u7684\u97fb\u6bcd\u6216\u975c\u97f3\uff0c\u5728\u6b64\u4e5f\u628a\u524d\u4e00\u97f3\u7bc0\u97fb\u6bcd\u5c3e\u7aef\u4e4b\u53ef\u80fd\u767c\u97f3\u53e3\u5f62\u5206\u6210 11 \u985e\uff0c\u5982\u8868 1 \u6240\u793a\u3002\u53e6\u4e00\u7a2e\u60c5\u6cc1\uff0c\u7576\u97fb\u6bcd\u5de6\u908a\u9047\u5230\u7684\u662f\u672c\u97f3\u7bc0\u7684\u8072\u6bcd\u6642\uff0c\u5728\u6b64\u6211\u5011\u6839\u64da\u8072\u6bcd\u7684\u767c\u97f3 \u4f4d\u7f6e\u628a\u53ef\u80fd\u9047\u5230\u7684\u8072\u6bcd\u5206\u6210 6 \u985e\uff0c\u8a73\u7d30\u5206\u985e\u65b9\u5f0f\u5982\u8868 3 \u6240\u5217\u3002\u8209\u4f8b\u4f86\u8aaa\uff0c/b/, /p/, /m/, /f/ \u7684\u767c\u97f3\u4f4d\u7f6e\u7686\u5728\u5634\u5507\uff0c\u6240\u4ee5\u5b83\u5011\u90fd\u88ab\u5206\u985e\u81f3\"b\"\u985e\u5225\uff1b\u540c\u6a23\u9053\u7406\uff0c/d/, /t/, /n/, /l/\u7684\u767c\u97f3\u4f4d \u7f6e\u7686\u5728\u9f52\u69fd\uff0c\u6240\u4ee5\u628a\u5b83\u5011\u90fd\u5206\u985e\u81f3\"d\"\u985e\u5225\u3002 \u8868 3. \u8072\u6bcd\u4f9d\u767c\u97f3\u4f4d\u7f6e\u4e4b\u5206\u985e Index 0 1 2 3 4 5 Consonant Classes b \u3105 d \u3109 z \u3117 zh \u3113 j \u3110 g \u310d \u8003\u616e\u76ee\u524d\u97f3\u7bc0\u97fb\u6bcd\u7684\u53f3\u908a\u53ef\u80fd\u9047\u5230\u7684\u8a9e\u97f3\u55ae\u5143\uff0c\u7b2c\u4e00\u7a2e\u60c5\u6cc1\u662f\uff0c\u5f8c\u63a5\u7684\u97f3\u7bc0\u6c92\u6709\u8072 \u6bcd\uff0c\u5728\u6b64\u6211\u5011\u628a\u5f8c\u63a5\u97f3\u7bc0\u7684\u97fb\u6bcd\u958b\u982d\u4e4b\u53ef\u80fd\u767c\u97f3\u53e3\u5f62\u5206\u6210 9 \u985e\uff0c\u8a73\u7d30\u5206\u985e\u65b9\u5f0f\u5982\u8868 1 \u4e2d \u7684 9 \u500b\u985e\u5225\uff0c\u4f46\u4e0d\u5305\u542b/n/\u548c/ng/\uff1b\u7b2c\u4e8c\u7a2e\u60c5\u6cc1\u662f\uff0c\u5f8c\u63a5\u7684\u97f3\u7bc0\u5177\u6709\u8072\u6bcd\uff0c\u5728\u6b64\u6211\u5011\u628a\u5f8c \u63a5\u97f3\u7bc0\u8072\u6bcd\u7684\u53ef\u80fd\u767c\u97f3\u53e3\u5f62\u5206\u6210 6 \u985e\uff0c\u8a73\u7d30\u5206\u985e\u65b9\u5f0f\u5982\u8868 3 \u6240\u5217\uff1b\u7b2c\u4e09\u7a2e\u60c5\u6cc1\u662f\uff0c\u5f8c\u63a5 \u97f3\u7bc0\u4e0d\u5b58\u5728(\u5373\u76ee\u524d\u97f3\u7bc0\u662f\u8a9e\u53e5\u7684\u6700\u5f8c\u97f3\u7bc0)\uff0c\u76f8\u7576\u65bc\u5f8c\u9762\u929c\u63a5\u7684\u662f\u975c\u97f3\u3002\u7576\u628a\u97fb\u6bcd\u524d\u5f8c \u53ef\u80fd\u9047\u5230\u7684\u8072\u3001\u97fb\u6bcd\u767c\u97f3\u53e3\u5f62\u4f5c\u4e86\u5206\u985e\u4e4b\u5f8c\uff0c\u518d\u8003\u616e\u570b\u8a9e\u5171\u6709 37 \u500b\u97fb\u6bcd\uff0c\u7531\u6b64\u53ef\u63a8\u7b97\u51fa \u570b\u8a9e\u97fb\u6bcd\u53ef\u80fd\u7d44\u5408\u51fa\u7684\u6587\u8108\u6578\u91cf\u662f(11+6+1) \u00d7 37 \u00d7 (9+6+1)=10,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\u7dd2\u8ad6", "sec_num": "1." } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Regularization techniques for discrete cepstrum estimation", "authors": [ { "first": "O", "middle": [], "last": "Capp\u00e9", "suffix": "" }, { "first": "&", "middle": [ "E" ], "last": "Moulines", "suffix": "" } ], "year": 1996, "venue": "IEEE Signal Processing Letters", "volume": "3", "issue": "4", "pages": "100--102", "other_ids": {}, "num": null, "urls": [], "raw_text": "Capp\u00e9, O., & E. 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In Proc. 6th ISCA Workshop on Speech Synthesis, Bonn, Germany, 294-299.", "links": null } }, "ref_entries": { "TABREF1": { "type_str": "table", "content": "
\u6211\u5011\u9080\u8acb\u4e86\u4e00\u4f4d\u6210\u5e74\u7537\u6027\u81f3\u9694\u97f3\u9304\u97f3\u5ba4\u9304\u88fd 1,208 \u500b\u8a9e\u53e5\u7684\u767c\u97f3\uff0c\u9019\u4e9b\u8a9e\u53e5\u7684\u8173\u672c \u6bcd)\u7684\u5de6\u53f3\u6587\u8108\u4f5c\u5206\u985e\uff0c\u4ee5\u964d\u4f4e\u6587\u8108\u7d44\u5408\u4e4b\u6578\u91cf\uff1b\u6b64\u5916\uff0c\u66f4\u9032\u4e00\u6b65\u7814\u7a76\u63d0\u51fa\u6587\u8108\u76f8\u4f9d\u4e4b\u534a |
(script)\u662f\u96a8\u6a5f\u5730\u5f9e\u7121\u95dc\u7684\u6587\u7ae0\u4e2d\u6311\u9078\u51fa\uff0c\u7e3d\u8a08\u6709 10,173 \u500b\u97f3\u7bc0\uff0c\u800c\u9304\u97f3\u7684\u53d6\u6a23\u7387\u70ba 22,050 \u6bb5\u5f0f HMM \u7d50\u69cb\uff0c\u4ee5\u4fbf\u5728\u6709\u9650\u8a9e\u6599\u7684\u60c5\u6cc1\u4e0b\uff0c\u638c\u63e1\u4e00\u500b\u8a9e\u97f3\u55ae\u5143\u7684\u6587\u8108\u76f8\u4f9d\u983b\u8b5c\u7279\u6027\u3002 |
Hz\u3002\u70ba\u4e86\u6a19\u8a18\u9019\u4e9b\u8a9e\u53e5\u4e2d\u5404\u97f3\u7bc0\u7684\u62fc\u97f3\u7b26\u865f\uff0c\u6211\u5011\u9996\u5148\u4f7f\u7528 HTK \u5957\u4ef6\u4f86\u4f5c forced \u5982\u6b64\u7d50\u5408\u5169\u8005\uff0c\u7528\u4ee5\u6539\u9032\u5408\u6210\u8a9e\u97f3\u7684\u6d41\u66a2\u5ea6\u3002 |
alignment \u8655\u7406\uff0c\u800c\u5f97\u5230\u521d\u6b65\u7684\u97f3\u7bc0\u908a\u754c\u4e4b\u6a19\u8a18\uff0c\u7136\u5f8c\u4ee5\u4eba\u5de5\u64cd\u4f5c WaveSurfer \u8edf\u9ad4\u53bb\u66f4 \u70ba\u4e86\u8a55\u4f30\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u7684\u534a\u6bb5\u5f0f HMM \u7d50\u69cb\uff0c\u6211\u5011\u9032\u884c\u4e86\u5169\u7a2e\u5be6\u9a57\uff0c\u5373\u983b\u8b5c\u8ddd\u96e2\u91cf |
\u6b63\u932f\u8aa4\u7684\u97f3\u7bc0\u908a\u754c\u6a19\u8a18\u3002 \u6e2c\u548c\u6d41\u66a2\u5ea6\u807d\u6e2c\uff0c\u91cf\u6e2c\u51fa\u7684\u5e73\u5747\u983b\u8b5c\u8ddd\u96e2\u986f\u793a\uff0c\u4f7f\u7528\u534a\u6bb5\u5f0f HMM \u7d50\u69cb\u6240\u5408\u6210\u51fa\u7684\u8a9e\u53e5\uff0c |
\u5927\u5e45\u964d\u4f4e\u6240\u9700\u7684\u8a13\u7df4\u8a9e\u6599\u6578\u91cf\uff0c\u5982 HTS \u8edf\u9ad4\u5c31\u662f\u63a1\u53d6\u6b64\u7a2e\u4f5c\u6cd5\u3002\u4e0d\u904e\uff0c\u672c\u8ad6\u6587\u63a1\u53d6\u53e6\u5916 \u4e00\u7a2e\u7814\u7a76\u65b9\u5411(approach)\uff0c\u5c31\u662f\u5148\u4f9d\u64da\u767c\u97f3\u77e5\u8b58\u4f86\u5c0d\u8072\u3001\u97fb\u6bcd\u4f5c\u5206\u985e(\u9019\u76f8\u7576\u65bc\u5c0d HMM \u4f5c\u5206\u7fa4)\uff0c\u518d\u7814\u7a76\u65b0\u7684 HMM \u7d50\u69cb\u4e4b\u8a2d\u8a08\uff0c\u4ee5\u4fbf\u89e3\u6c7a\u524d\u8ff0\u7684\u9700\u6c42\u5927\u91cf\u8a13\u7df4\u8a9e\u6599\u7684\u554f\u984c\u3002 3. \u5efa\u69cb\u65b0\u7684HMM\u7d50\u69cb \u5982\u5716 3 4. \u5be6\u9a57\u8a55\u4f30 \u5728\u672c\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u61c9\u7528\u97f3\u7d20\u7684\u767c\u97f3\u77e5\u8b58\u65bc\u53d6\u4ee3\u6c7a\u7b56\u6a39\uff0c\u4f86\u5c0d\u4e00\u500b\u8a9e\u97f3\u55ae\u5143(\u8072\u6bcd\u6216\u97fb 4.1 \u8a13\u7df4\u968e\u6bb5 \u6211\u5011\u5c07\u5404\u500b\u97f3\u7bc0\u7684\u97f3\u6a94\u5207\u5272\u6210\u4e00\u5e8f\u5217\u7684\u97f3\u6846\uff0c\u97f3\u6846\u9577\u5ea6\u8a2d\u70ba 512 \u500b\u6a23\u672c\u9ede\uff0c\u800c\u97f3\u6846 \u4f4d\u79fb\u8a2d\u70ba 128 \u500b\u6a23\u672c\u9ede\u3002\u6bcf\u500b\u97f3\u6846\u7d93\u5206\u6790\u8a08\u7b97\u5f8c\u64f7\u53d6\u51fa 39 \u500b\u983b\u8b5c\u53c3\u6578 c 0 , c 1 , \u2026, c 38 \uff0c\u5be6 \u969b\u4e0a\u662f\u96e2\u6563\u5012\u983b\u8b5c\u4fc2\u6578(discrete cepstral coefficients, DCC) (Capp\u00e9 & Moulines, 1996)\uff0c DCC \u4fc2\u6578\u7684\u64f7\u53d6\u65b9\u6cd5\uff0c\u8acb\u53c3\u8003\u6211\u5011\u5148\u524d\u7684\u7814\u7a76\u8ad6\u6587(Gu & Tsai, 2009)\u3002\u6b64\u5916\uff0c\u4e00\u500b\u97f3\u6846 \u7684\u9031\u671f\u6027\u8cc7\u8a0a\u4e5f\u6703\u88ab\u8a18\u9304\u5728\u53e6\u4e00\u500b\u7dad\u5ea6\u4e2d\uff0c\u5373\u5b58\u5165 c 39 \uff0c\u5982\u679c\u4e00\u500b\u97f3\u6846\u88ab\u5075\u6e2c\u51fa\u662f\u9031\u671f\u6027 \u7684\uff0c\u5247\u8a2d\u5b9a c 39 \u4e4b\u8a13\u7df4\uff0c\u6211\u5011\u4f9d\u64da\u5206\u6bb5\u5f0f K \u4e2d\u5fc3(segmental K-means)\u6f14\u7b97\u6cd5(Rabiner & Juang, 1993)\uff0c\u53bb \u767c\u5c55\u4e86\u8a13\u7df4\u7a0b\u5f0f\u3002 4.2 \u8a9e\u97f3\u5408\u6210\u8655\u7406 \u672c\u8ad6\u6587\u88fd\u4f5c\u7684\u570b\u8a9e\u8a9e\u97f3\u5408\u6210\u7cfb\u7d71\uff0c\u5176\u5408\u6210\u968e\u6bb5\u4e4b\u8655\u7406\u6d41\u7a0b\u5982\u5716 1 \u6240\u793a\uff0c\u5340\u584a(a)\u4f5c \u6587 \u53e5\u5206\u6790(text analysis)\uff0c\u6bcf\u6b21\u6703\u5f9e\u8f38\u5165\u7684\u6a94\u6848\u8b80\u53d6\u4e00\u500b\u6587\u53e5\u9032\u4f86\uff0c\u7136\u5f8c\u4ee5\u67e5\u8a5e\u5178\u53ca\u6aa2\u67e5\u6578 \u500b\u69cb\u8a5e\u898f\u5247\u7684\u65b9\u5f0f\u53bb\u4f5c\u5256\u6790\uff0c\u628a\u8b80\u5165\u7684\u6587\u53e5\u5207\u5272\u6210\u4e00\u5e8f\u5217\u7684\u8a5e\u8a9e(words)\uff0c\u4e26\u4e14\u6bcf\u4e00\u500b\u8a5e \u8a9e\u7d93\u7531\u67e5\u8a5e\u5178\u4e5f\u53ef\u5f97\u77e5\u5b83\u7684\u62fc\u97f3\u7b26\u865f\u3002\u63a5\u8457\uff0c\u5728\u5340\u584a(b)\u7522\u751f\u5404\u97f3\u7bc0\u4e4b\u97f3\u9ad8\u8ecc\u8de1(pitch contour)\u53ca\u6642\u9577(duration)\u503c\uff0c\u5c0d\u65bc\u6bcf\u4e00\u500b\u97f3\u7bc0\uff0c\u5148\u6e96\u5099\u597d\u5b83\u7684\u6587\u8108\u8cc7\u6599\u9805\uff0c\u518d\u5c07\u6587\u8108\u8cc7\u6599 \u8f38\u5165\u81f3\u5169\u500b\u985e\u795e\u7d93\u7db2\u8def(ANN)\uff0c\u4ee5\u5206\u5225\u9810\u6e2c\u51fa\u97f3\u9ad8\u8ecc\u8de1\u53c3\u6578\u548c\u6642\u9577\u53c3\u6578\u7684\u503c\uff0c\u95dc\u65bc ANN \u7684\u8f38\u51fa/\u8f38\u5165\u8cc7\u6599\u9805\u3001\u53ca\u7d50\u69cb\u7684\u7d30\u7bc0\uff0c\u8acb\u53c3\u8003\u6211\u5011\u5148\u524d\u7684\u7814\u7a76\u8ad6\u6587(Gu & Wu, 2009)\u3002 \u5728\u5340\u584a(c)\u6311\u9078\u8072\u6bcd\u3001\u97fb\u6bcd\u4e4b HMM \u6a21\u578b\uff0c\u9996\u5148\u4f9d\u64da\u5404\u500b\u97f3\u7bc0\u7684\u62fc\u97f3\u7b26\u865f\u53bb\u67e5\u8a62\u51fa\u5c0d \u61c9\u7684\u8072\u6bcd\u548c\u97fb\u6bcd\u4e4b\u62fc\u97f3\u7b26\u865f\u8207\u7de8\u865f\uff0c\u4e26\u4e14\u6c7a\u5b9a\u8072\u3001\u97fb\u6bcd\u5728\u8868 1\u3001\u8868 2\u3001\u8207\u8868 3 \u7684\u5206\u985e\u7de8\u865f\uff0c \u7136\u5f8c\u4f9d\u64da\u5404\u500b\u55ae\u5143(\u8072\u6bcd\u6216\u97fb\u6bcd)\u7684\u7de8\u865f\u548c\u5176\u524d\u5f8c\u6587\u8108\u7684\u5206\u985e\u7de8\u865f\uff0c\u5f9e\u8a13\u7df4\u51fa\u7684\u534a\u6bb5\u5f0f HMM \u6a21\u578b\u4e2d\uff0c\u627e\u51fa\u4e00\u500b\u55ae\u5143(\u8072\u6bcd\u6216\u97fb\u6bcd)\u5c0d\u61c9\u7684\u524d\u3001\u5f8c\u5169\u500b\u534a\u6bb5\u5f0f HMM\uff0c\u63a5\u8457\u628a\u8072\u3001 \u4f86\u8a08\u7b97\u5404\u500b\u97f3\u6846\u7684 DCC \u4fc2\u6578\u3002 \u5728\u5340\u584a(f)\u8a08\u7b97\u5404\u500b\u97f3\u6846\u7684\u97f3\u9ad8\u503c\uff0c\u4e00\u500b\u6709\u8072\u55ae\u5143(\u4f8b\u5982\u8072\u6bcd/m/\u548c\u97fb\u6bcd/a/)\u7684\u5404\u500b\u97f3\u6846 \u90fd\u5fc5\u9808\u88ab\u6307\u6d3e\u4e00\u500b\u97f3\u9ad8\u983b\u7387\u503c(\u55ae\u4f4d Hz)\uff0c\u5728\u6b64\u6211\u5011\u62ff ANN \u7522\u751f\u7684\u57fa\u9031\u8ecc\u8de1\u53c3\u6578\u53bb\u4f5c\u62c9 \u683c\u862d\u65e5\u5167\u63d2(Lagrange interpolation)\uff0c\u4ee5\u6c42\u5f97\u5404\u500b\u97f3\u6846\u7684\u97f3\u9ad8\u983b\u7387\u503c\u3002\u63a5\u8457\uff0c\u5728\u5340\u584a(g)\u63a1 \u7528 HNM \u4fe1\u865f\u6a21\u578b\u4f5c\u8a9e\u97f3\u4fe1\u865f\u5408\u6210\uff0c\u6211\u5011\u628a\u4e00\u500b\u55ae\u5143\u5404\u97f3\u6846\u7684 DCC \u4fc2\u6578\u8207\u97f3\u9ad8\u983b\u7387\u503c\uff0c \u6309\u7167\u97f3\u6846\u6b21\u5e8f\u9010\u500b\u97f3\u6846\u9001\u7d66 HNM \u8a9e\u97f3\u4fe1\u865f\u5408\u6210\u6a21\u7d44\uff0c\u53bb\u5408\u6210\u51fa\u8a9e\u97f3\u4fe1\u865f\uff0c\u95dc\u65bc HNM \u4fe1 \u865f\u5408\u6210\u4e4b\u7d30\u90e8\u8655\u7406\u65b9\u6cd5\uff0c\u53ef\u53c3\u8003\u6211\u5011\u5148\u524d\u7684\u7814\u7a76\u8ad6\u6587(Gu & Tsai, 2013; Gu & Tsai, 2009)\u3002 4.3 \u983b\u8b5c\u8ddd\u96e2\u91cf\u6e2c \u5728\u6b64\u4ee5\u4ee3\u865f SYC \u8207 SYD \u8868\u793a\u672c\u7814\u7a76\u6240\u5efa\u9020\u7684\u5169\u500b\u570b\u8a9e\u8a9e\u97f3\u5408\u6210\u7cfb\u7d71\uff0cSYC \u8868\u793a\u5b8c \u6574\u7684\u7cfb\u7d71\uff0c\u5c0d\u65bc\u4e00\u500b\u97f3\u7bc0\u7684\u524d\u63a5\u8207\u5f8c\u63a5\u6587\u8108\u90fd\u7d0d\u5165\u8003\u616e\uff0c\u9019\u610f\u5473\u8072\u6bcd\u7684\u524d\u534a\u6bb5 HMM \u7684 \u5de6\u6587\u8108\u6709\u4f5c\u5340\u5206(\u4f8b\u5982\u5716 4 \u4e4b GY_X)\uff0c\u4e26\u4e14\u97fb\u6bcd\u7684\u5f8c\u534a\u6bb5 HMM \u7684\u53f3\u6587\u8108\u4e5f\u6709\u4f5c\u5340\u5206(\u5982 \u5716 5 \u4e4b HY_Z)\uff0c\u6240\u4ee5\u5c0d\u65bc\u5404\u500b\u4e0d\u540c\u7684\u6587\u8108\u6a23\u5f0f\u5206\u985e(\u5982 CX)\uff0c\u5c31\u9700\u8981\u53bb\u5efa\u9020\u4e00\u500b\u5c0d\u61c9\u7684\u534a \u6bb5\u5f0f HMM (\u5982 GY_X)\u3002\u76f8\u53cd\u5730\uff0c\u5728\u7c21\u5316\u7684\u7cfb\u7d71 SYD \u88e1\uff0c\u5c0d\u65bc\u4e00\u500b\u97f3\u7bc0\u7684\u524d\u63a5\u8207\u5f8c\u63a5\u6587 \u8108\u5c31\u4e0d\u53bb\u4f5c\u5340\u5206\u4e86\uff0c\u4ea6\u5373\u8072\u6bcd\u7684\u524d\u534a\u6bb5 HMM \u4e0d\u53bb\u5340\u5206\u5b83\u7684\u5de6\u6587\u8108\uff0c\u5982\u6b64\u4e00\u500b\u8072\u6bcd\u5c31\u50c5 \u9700\u8a13\u7df4\u51fa\u4e00\u500b\u524d\u534a\u6bb5\u4e4b HMM\uff0c\u7136\u800c\u8072\u6bcd\u5f8c\u534a\u6bb5\u4e4b HMM\uff0c\u5247\u4ecd\u7136\u9700\u8a13\u7df4\u51fa\u6578\u500b\u534a\u6bb5\u5f0f HMM\uff0c\u4ee5\u5340\u5206\u53f3\u6587\u8108\uff1b\u985e\u4f3c\u9053\u7406\uff0c\u97fb\u6bcd\u7684\u5f8c\u534a\u6bb5 HMM \u5c31\u4e0d\u53bb\u5340\u5206\u5b83\u7684\u53f3\u6587\u8108\uff0c\u5982\u6b64\u4e00 \u500b\u97fb\u6bcd\u5c31\u53ea\u9700\u8a13\u7df4\u51fa\u4e00\u500b\u5f8c\u534a\u6bb5\u4e4b HMM\uff0c\u7136\u800c\u97fb\u6bcd\u524d\u534a\u6bb5\u4e4b HMM\uff0c\u5247\u4ecd\u7136\u9700\u8a13\u7df4\u51fa\u6578 \u500b\u534a\u6bb5\u5f0f HMM\uff0c\u4ee5\u5340\u5206\u5de6\u6587\u8108\u3002\u6b64\u5916\uff0c\u4ee5 SYP \u8868\u793a\u5148\u524d\u7814\u7a76\u88e1\u6240\u5efa\u9020\u7684\u570b\u8a9e\u8a9e\u97f3\u5408\u6210 \u7cfb\u7d71(Gu et al., 2010)\uff0c\u5728 SYP \u7cfb\u7d71\u88e1\uff0c\u6211\u5011\u5c0d\u65bc\u6bcf\u4e00\u7a2e\u570b\u8a9e\u97f3\u7bc0\u90fd\u5efa\u9020\u4e86\u4e00\u500b\u6216\u6578\u500b\u97f3 \u7bc0\u5bec\u5ea6\u7684 HMM\uff0c\u81f3\u65bc\u4e00\u7a2e\u97f3\u7bc0\u6240\u5efa\u9020\u7684 HMM \u500b\u6578\uff0c\u5247\u548c\u8a72\u97f3\u7bc0\u5728\u8a13\u7df4\u8a9e\u53e5\u4e2d\u7684\u767c\u97f3 \u6b21\u6578\u6709\u95dc\u3002\u5728\u672c\u7814\u88e1\uff0c\u6211\u5011\u4f7f\u7528\u76f8\u540c\u7684\u8a13\u7df4\u8a9e\u53e5\uff0c\u4f86\u8a13\u7df4\u9019\u4e09\u500b\u7cfb\u7d71: SYC\u3001SYD \u548c SYP\u3002 \u85c9\u7531\u9019\u4e09\u500b\u7cfb\u7d71\uff0c\u6211\u5011\u5c31\u53ef\u4ee5\u628a\u9304\u97f3\u8a9e\u53e5\u8207\u5408\u6210\u8a9e\u53e5\u76f8\u5c0d\u61c9\u7684\u97f3\u6846\u62ff\u53bb\u4f5c\u983b\u8b5c\u8ddd\u96e2 \u7684\u8a08\u7b97\u3002\u8a73\u7d30\u60c5\u5f62\u662f\uff0c\u628a 50 \u53e5\u6e2c\u8a66\u8a9e\u53e5\u7684\u6a19\u8a18\u6a94\u9010\u4e00\u8f38\u5165\u7d66 SYC\u3001SYD \u548c SYP \u7cfb\u7d71\u53bb \u8655\u7406\uff0c\u4ee5\u53d6\u5f97\u4e09\u500b\u7cfb\u7d71\u5c0d\u61c9\u65bc\u5404\u53e5\u6e2c\u8a66\u8a9e\u53e5\u7684\u7684\u5408\u6210\u97f3\u6a94\uff0c\u7136\u5f8c\u5206\u5225\u62ff\u5404\u53e5\u7684\u9304\u97f3\u8a9e\u53e5 \u53bb\u548c\u5408\u6210\u8a9e\u53e5\u53bb\u4f5c DCC \u5206\u6790\uff0c\u4ee5\u8a08\u7b97\u51fa\u5169\u500b DCC \u983b\u8b5c\u7279\u5fb5\u5411\u91cf\u7684\u5e8f\u5217\u3002\u63a5\u8457\uff0c\u5075\u6e2c\u5169 DCC \u5e8f\u5217\u4e2d\u5404\u7d44\u5c0d\u61c9\u97f3\u6846\u662f\u5426\u90fd\u70ba\u6709\u8072\uff0c\u82e5\u5c0d\u61c9\u7684\u97f3\u6846\u90fd\u5075\u6e2c\u70ba\u6709\u8072\uff0c\u5c31\u62ff\u8a72\u7d44\u97f3\u6846\u53bb \u8a08\u7b97\u97f3\u6846\u4e4b\u9593\u7684 DCC \u5411\u91cf\u5e7e\u4f55\u8ddd\u96e2\uff0c\u7136\u5f8c\u6211\u5011\u62ff 50 \u53e5\u6e2c\u8a66\u8a9e\u53e5\u7684\u6240\u6709\u6709\u8072\u97f3\u6846\u7b97\u51fa\u7684 \u5e7e\u4f55\u8ddd\u96e2\uff0c\u8a08\u7b97\u51fa\u4e00\u500b\u8de8\u8a9e\u53e5\u7684\u5e73\u5747\u8ddd\u96e2\u3002 SYC SYD SYP Avg. dist. 0.633 0.640 0.732 4.4 \u4e3b\u89c0\u807d\u6e2c\u5be6\u9a57 \u807d\u6e2c\u5be6\u9a57\u4f7f\u7528\u4e00\u7bc7\u8a13\u7df4\u8a9e\u53e5\u6c92\u6709\u7528\u5230\u7684\u77ed\u6587\uff0c\u8a72\u77ed\u6587\u5305\u62ec 70 \u500b\u97f3\u7bc0\uff0c\u6211\u5011\u5c07\u5b83\u5206 \u5225\u8f38\u5165\u5230\u4e09\u500b\u7cfb\u7d71\u53bb\u5408\u6210\u51fa\u8a9e\u97f3\u97f3\u6a94\uff0c\u5728\u6b64\u5206\u5225\u4ee5 WC\u3001WD \u548c WP \u4f86\u8868\u793a SYC\u3001SYD \u548c SYP \u9019 \u4e09 \u500b \u7cfb \u7d71 \u6240 \u5408 \u6210 \u7684 \u97f3 \u6a94 \uff0c \u9019 \u4e9b \u97f3 \u6a94 \u53ef \u5230 \u5982 \u4e0b \u7684 \u7db2 \u5740 \u53bb \u4e0b \u8f09 \u8207 \u8a66 \u807d : \u8005(\u524d\u8005)\u6bd4\u524d\u8005(\u5f8c\u8005)\u6d41\u66a2\u5f88\u591a\uff0c1(-1) \u5206\u8868\u793a\u5f8c\u8005(\u524d\u8005)\u6bd4\u524d\u8005(\u5f8c\u8005)\u7a0d\u5fae\u6d41\u66a2\uff0c0 \u5206\u8868 \u793a\u5206\u8fa8\u4e0d\u51fa\u4f86\u3002 \u4e09\u6b21\u807d\u6e2c\u5be6\u9a57\u4e4b\u5f8c\uff0c\u6211\u5011\u4f9d\u97f3\u6a94\u64ad\u653e\u6b21\u5e8f\u4f86\u8abf\u6574\u5206\u6578\u7684\u6b63\u8ca0\u865f\uff0c\u7136\u5f8c\u8a08\u7b97\u51fa\u5404\u6b21\u5be6 \u9a57\u7684\u5e73\u5747\u5206\u6578\u548c\u6a19\u6e96\u5dee\uff0c\u7d50\u679c\u5f97\u5230\u5982\u8868 5 \u6240\u793a\u7684\u6578\u503c\u3002\u5f9e\u8868 5 \u53ef\u77e5\u7b2c\u4e00\u6b21\u548c\u7b2c\u4e8c\u6b21\u5be6\u9a57 \u7684\u5e73\u5747\u5206\u6578\u70ba-0.833 \u548c-0.WC vs. WD WC vs. WP WD vs. WP AVG -0.833 -0.417 0.250 STD 0.718 0.900 0.866 5. \u7d50\u8a9e \u548c\u539f\u59cb\u9304\u97f3\u8a9e\u53e5\u4e4b\u9593\u7684\u983b\u8b5c\u8ddd\u96e2\uff0c\u53ef\u5f9e 0.732 \u6e1b\u5c11\u5230 0.633\uff1b\u6b64\u5916\uff0c\u807d\u6e2c\u5be6\u9a57\u7684\u7d50\u679c\u986f\u793a\uff0c \u4f7f\u7528\u534a\u6bb5\u5f0f HMM \u6240\u5408\u6210\u51fa\u7684\u8a9e\u97f3\uff0c\u6bd4\u4f7f\u7528\u53e6\u5916\u5169\u7a2e HMM \u7d50\u69cb\u7684\u8f03\u70ba\u6d41\u66a2\uff0c\u6240\u4ee5\u5728\u8a13 \u7df4\u8a9e\u53e5\u4e0d\u5145\u8db3\u7684\u60c5\u6cc1\u4e0b\uff0c\u534a\u6bb5\u5f0f HMM \u7d50\u69cb\u78ba\u5be6\u53ef\u6539\u9032\u5408\u6210\u8a9e\u97f3\u7684\u6d41\u66a2\u5ea6\u3002 \u672a\u4f86\u6211\u5011\u53ef\u5728\u76f8\u540c\u8a13\u7df4\u8a9e\u6599\u7684\u60c5\u6cc1\u4e0b\uff0c\u6bd4\u8f03\u6211\u5011\u7cfb\u7d71\u7684\u5408\u6210\u8a9e\u97f3\u8207 HTS \u8edf\u9ad4\u7684\u5408\u6210 \u8a9e\u97f3\uff0c\u89c0\u5bdf\u5169\u8005\u5728\u5ba2\u89c0\u983b\u8b5c\u8ddd\u96e2\u548c\u4e3b\u89c0\u807d\u6e2c\u4e0a\u7684\u5dee\u7570\u3002\u53e6\u5916\uff0c\u672c\u8ad6\u6587\u8457\u91cd\u65bc\u6539\u9032\u8a9e\u97f3\u55ae \u5143\u4e4b\u9593\u983b\u8b5c\u929c\u63a5\u4e0a\u7684\u6d41\u66a2\u5ea6\uff0c\u672a\u4f86\u53ef\u518d\u8003\u616e\u53bb\u6539\u9032\u97fb\u5f8b\u65b9\u9762\u7684\u6d41\u66a2\u5ea6\uff0c\u4ee5\u66f4\u70ba\u63d0\u5347\u7cfb\u7d71 \u6574\u9ad4\u7684\u6d41\u66a2\u5ea6\u3002 \u8868 4 System \u81f4\u8b1d \u611f\u8b1d\u570b\u79d1\u6703\u8a08\u756b\u4e4b\u7d93\u8cbb\u652f\u63f4\uff0c\u570b\u79d1\u6703\u8a08\u756b\u7de8\u865f: NSC 102-2221-E-011-129\u3002 http:/\u5011\u8981\u6c42\u4ed6\u7d66\u4e00\u500b\u5206\u6578\u4f86\u986f\u793a\u6d41\u66a2\u5ea6\u7684\u807d\u6e2c\u7d50\u679c\uff0c\u8a55\u5206\u7684\u7bc4\u570d\u70ba-2 \u5230 2 \u5206\uff0c2(-2)\u5206\u8868\u793a\u5f8c \u53c3\u8003\u6587\u737b |