| { | |
| "paper_id": "O12-2002", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:03:04.368130Z" | |
| }, | |
| "title": "Variable Speech Rate Mandarin Chinese Text-to-Speech System", | |
| "authors": [ | |
| { | |
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| "institution": "National Chiao Tung University", | |
| "location": { | |
| "settlement": "Hsinchu", | |
| "country": "Taiwan" | |
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| "email": "" | |
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| "last": "\u3001\u9ec3\u555f\u5168", | |
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| "affiliation": { | |
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| "institution": "National Chiao Tung University", | |
| "location": { | |
| "settlement": "Hsinchu", | |
| "country": "Taiwan" | |
| } | |
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| "email": "" | |
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| "first": "\u3001\u4f59\u79c0\u654f", | |
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| "institution": "National Chiao Tung University", | |
| "location": { | |
| "settlement": "Hsinchu", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chen-Yu", | |
| "middle": [], | |
| "last": "Chiang", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Chiao Tung University", | |
| "location": { | |
| "settlement": "Hsinchu", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "cychiang@mail.ntpu.edu.tw" | |
| }, | |
| { | |
| "first": "Qi-Quan", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yih-Ru", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "yrwang@cc.nctu.edu.tw" | |
| }, | |
| { | |
| "first": "Hsiu-Min", | |
| "middle": [], | |
| "last": "Yu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Horng", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "schen@mail.nctu.edu.tw" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper presents an Hidden Markov Model (HMM)-based variable speech rate Mandarin Chinese text-to-speech (TTS) system. In this system, parameters of spectrum, fundametal frequency and state duration are generated by a context dependent HMM (CDHMM) whose model parameters are linear-interpolated from those of three CDHMMs trained by corpora in three different speech rates (SRs), i.e. fast, medium and slow. In addition, three decision tree (DT)-based pause break predictors trained by using the three SR corpora are used to interpolate the probabilities for inserting pause breaks. The performance of the proposed TTS system were evaluated by several objective and subjective tests. Experimental results suggested that coherence between interpolation weights for CDHMMs and DT-based pasue predictors is crutial for naturalness of the synthesis speech in variable SR. We believe that the proposed variable speech rate Mandarin Chinese TTS system is more suitable than conventional fixed SR TTS systems for applications of human-machine interaction.", | |
| "pdf_parse": { | |
| "paper_id": "O12-2002", | |
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| "abstract": [ | |
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| "text": "This paper presents an Hidden Markov Model (HMM)-based variable speech rate Mandarin Chinese text-to-speech (TTS) system. In this system, parameters of spectrum, fundametal frequency and state duration are generated by a context dependent HMM (CDHMM) whose model parameters are linear-interpolated from those of three CDHMMs trained by corpora in three different speech rates (SRs), i.e. fast, medium and slow. In addition, three decision tree (DT)-based pause break predictors trained by using the three SR corpora are used to interpolate the probabilities for inserting pause breaks. The performance of the proposed TTS system were evaluated by several objective and subjective tests. Experimental results suggested that coherence between interpolation weights for CDHMMs and DT-based pasue predictors is crutial for naturalness of the synthesis speech in variable SR. We believe that the proposed variable speech rate Mandarin Chinese TTS system is more suitable than conventional fixed SR TTS systems for applications of human-machine interaction.", | |
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| "section": "Abstract", | |
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| "text": "\u53ca\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(HMM-based approach) (Tokuda et al., 2000 (Imai, 1983 ) \u8f38\u51fa\u5408\u6210\u51fa\u8a9e\u97f3\u8a0a\u865f\u3002 \u7576\u60f3\u8981\u4ee5\u73fe\u6709\u6a21\u578b\u53bb\u5408\u6210\u51fa\uf967\u540c\u7279\u6027\u7684\u8a9e\u97f3\u8a0a\u865f\uff0c\u5247\u53ef\uf9dd\u7528\u8abf\u6574\uf96b\uf969\u7684\u65b9\u5f0f\u9054\u5230\u76ee \u7684\uff0c\u5982\u5167\u63d2(interpolation methods) (Yoshimura et al., 2000) \u3001\u8abf\u9069(adaptation methods) (Tamura et al., 2001 (Yu et al., 2007) \u7684\u65b9\u6cd5\u63d0\u4f9b\uf9ba bottom-up \u7684\u65b9\u5f0f\u5206\u6790\uff0c\u50c5\u5f9e\u97f3\u7bc0\u5c64\u6b21\u8a0e\uf941\u97f3\u9ad8\u8ecc\u8de1\u6703\u5ffd\uf976\u5230\u97fb \uf9d8\u7d50\u69cb\u4e0a\u5c64\u7684\u5f71\u97ff\uff1b\u81f3\u65bc (Li & Zu, 2008) \u548c (Tseng, 2008 ) \u7684\u968e\u5c64\u5f0f\u97fb\uf9d8\u67b6\u69cb\u5247\u63d0\u4f9b\u4e00 \u500b top-down \u7684\u5206\u6790\u65b9\u5f0f\uff0c\u5c0d\u65bc\u5e95\u5c64\u4e4b\u97f3\u7bc0\u5c64\u6b21\u5206\u6790\u8f03\u7f3a\u4e4f\uff0c\u6b64\u5916\uff0c\u50b3\u7d71\u97fb\uf9d8\u968e\u5c64\u7684\u7814\u7a76 \u90fd\u9700\u8981\u4eba\u5de5\u4e8b\u5148\u6a19\u8a18\u97fb\uf9d8\u908a\u754c\uff0c\u56e0\u6b64\uff0c\u5728\u6587\u737b (Chiang et al., 2009 (Yoshimura et al., 2000; Iwano et al., 2002) ", | |
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| "text": "(Tokuda et al., 2000", | |
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| "text": "(Imai, 1983", | |
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| "text": "(Yoshimura et al., 2000)", | |
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| "text": "(Tamura et al., 2001", | |
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| "text": "(Yu et al., 2007)", | |
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| "text": "(Li & Zu, 2008)", | |
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| "text": "(Tseng, 2008", | |
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| "end": 415, | |
| "text": "(Chiang et al., 2009", | |
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| "end": 440, | |
| "text": "(Yoshimura et al., 2000;", | |
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| "end": 460, | |
| "text": "Iwano et al., 2002)", | |
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| { | |
| "text": "= = \u00d7 \u2211 (1) \u5176\u4e2d i \u70ba\u6c7a\u7b56\u6a39\u7684 index (i=1\uff1a\u6162\uff0ci=2\uff1a\u4e2d\uff0ci=3\uff1a\u5feb)\uff1b i w \u70ba\u7b2c i \u500b\u6c7a\u7b56\u6a39\u6a21\u578b\u7684\u6b0a \u91cd\u503c\uff1b { } n sp \u2208 \u975c\u97f3\u505c\u9813, \u975e\u975c\u97f3\u505c\u9813 \u70ba\u7b2c n \u500b\u8a5e\u5f8c\u9762\u7684\u975c\u97f3\u505c\u9813\u8207\u5426\uff1b n L \u70ba\u6587\u8108\u8a9e\u8a00 \u8cc7\u8a0a\uff1b ( | ) i n n P sp L \u70ba\u7d93\u7531\u6587\u8108\u8a9e\u8a00\u8cc7\u8a0a ( n L ) \u7d44\u6210\u6c7a\u7b56\u6a39\u554f\u984c\u96c6\u5f8c\uff0c\u7531\u7b2c i \u500b\u6c7a\u7b56\u6a39\u7d50 \u69cb\uf9e8\uff0c\u627e\u5c0b\u5230\u5c0d\u61c9\u4e4b\uf96e\u7bc0\u9ede\u4e0b (leaf node) \u975c\u97f3\u505c\u9813\u548c\u975e\u975c\u97f3\u505c\u9813\u7684\u6a5f\uf961\u503c\u3002 \u800c\u7b2c\u4e8c\u7d44\u6b0a\u91cd\u503c\u5f71\u97ff\u8457\u983b\u8b5c\u3001\u97f3\u9577\u53ca\u57fa\u983b\uff0c\u5728\u8a9e\uf9be\u5206\u6790\u4e2d\u767c\u73fe\u8a9e\u901f\u8d8a\u5feb\uf967\u50c5\u97f3\u9577\u8b8a \u77ed\uff0c\u57fa\u983b\u4e5f\u6703\u96a8\u8457\uf925\u9ad8\uff0c\u76f4\u63a5\u5f71\u97ff\u5230\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u7684\uf96b\uf969\uff0c\u5f88\u76f4\u89c0\u5730\uff0c\u7576\u8abf\u6574\u6b0a\u91cd \u503c\u8d8a\u9760\u8fd1\u5feb\u901f\u8a9e\u901f\u8a9e\u97f3\u4e4b\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u6642\uff0c\u76f8\u5c0d\u65bc\u50c5\u4f7f\u7528\u6162\u901f\u8a9e\u97f3\u4e4b\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b \u6a21\u578b\uff0c\u6bcf\u500b\u97f3\u7bc0\u7684\u97f3\u9577\u6703\u8b8a\u77ed\u4e14\u97f3\u983b\u6703\u63d0\u9ad8\u3002\u4ee5\uf967\u540c\u6b0a\u91cd\u503c\u5167\u5dee\u4e09\u7a2e\u8a9e\u901f\u4e4b\u6a21\u578b\uf96b\uf969\u65b9 \u6cd5\u5982\u4e0b\u5f0f", | |
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| { | |
| "text": "\uff1a 3 1 i i i a = = \u00d7 \u2211 \u03bc \u03bc (2) 3 2 1 i i i a = = \u00d7 \u2211 U U (3) \u5176\u4e2d i \u70ba CDHMM \u6a21\u578b\u7684 index(i=1\uff1a\u6162\uff0ci=2\uff1a\u4e2d\uff0ci=3\uff1a\u5feb)\uff1b i a \u70ba\u7b2c i \u500b CDHMM \u6a21 \u578b\u7684\u6b0a\u91cd\u503c\uff0c i \u03bc \u53ca i U \u5206\u5225\u70ba CDHMM", | |
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| "bib_entries": { | |
| "BIBREF0": { | |
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| "TABREF1": { | |
| "text": "\u3001\u57fa\u983b\u6a21\u578b(F0 parameter model)\u53ca\u97f3\u9577\u6a21\u578b(duration model)\u3002\u6b32\u5408\u6210\u8a9e \u97f3\u6642\uff0c\uf9dd\u7528\u4e0a\u8ff0\u8a13\uf996\u597d\u7684\u4e09\u7a2e\u6a21\u578b\uff0c\u4f9d\u64da\u8f38\u5165\u6587\u672c\u7684\u8a9e\u8a00\uf96b\uf969\u6216\u9810\u4f30\u4e4b\u97fb\uf9d8\u6a19\u8a18\u627e\u5230\u9069 \u7576 CDHMM \u6a21\u578b\u4e26\uf905\u63a5\u4e4b\uff0c\u518d\u4ee5\u7279\u6b8a\u7684\u6f14\u7b97\u6cd5\u7531\uf905\u63a5\u4e4b CDHMM \uf96b\uf969\u7522\u751f frame spectrum \u53ca frame F0 \uf96b\uf969\uff0c\u6700\u5f8c\u5c07 spectrum \u548c f0 \uf96b\uf969\u8f38\u5165 MLSA \uf984\u6ce2\u5668(Mel Log Spectrum Approximation filter)", | |
| "num": null, | |
| "content": "<table><tr><td>\u53ef\u8b8a\u901f\u4e2d\u6587\u6587\u5b57\u8f49\u8a9e\u97f3\u7cfb\u7d71</td><td>29</td></tr><tr><td colspan=\"2\">\u97f3\u5408\u6210\u65b9\u6cd5\uff1b\u5927\u578b\u8a9e\uf9be\u5eab\u5408\u6210\u6cd5\u7531\uf93f\u88fd\u597d\u7684\u8a9e\uf9be\u5eab\u4e2d\uff0c\u6311\u9078\u9069\u7576\u7684\u8a9e\u97f3\u4fe1\u865f\u7247\u6bb5\uf905\u63a5\u5408</td></tr><tr><td colspan=\"2\">\u6210\uff0c\u56e0\u6b64\u53ef\u539f\u97f3\u91cd\u73fe\uff0c\u6709\u6975\u4f73\u7684\u5408\u6210\u97f3\u8cea\uff0c\u4f46\u662f\u5982\u679c\u8981\u5408\u6210\u51fa\uf967\u540c\u7279\u6027\u7684\u8a9e\u97f3\uff0c\u5982\uf967\u540c</td></tr><tr><td colspan=\"2\">\u8b1b\u8a71\u901f\ufa01\u53ca\u591a\u7a2e\u60c5\u7dd2\u7b49\u61c9\u7528\uff0c\u5247\u9808\uf93f\u88fd\u5927\uf97e\u7684\u8a9e\uf9be\u4f5c\u70ba\u6311\u9078\u55ae\u5143\u7684\u57fa\u790e\uff0c\u7136\u800c\u6b32\u6536\u96c6\uf967</td></tr><tr><td colspan=\"2\">\u540c\u7279\u6027\u4e4b\u8a9e\uf9be\u4e26\uf967\u5bb9\uf9e0\uff0c\u56e0\u6b64\uff0c\u5c0d\u65bc\u5408\u6210\uf967\u540c\u7279\u6027\u8a9e\u97f3\u7684\u61c9\u7528\uff0c\u55ae\u5143\u9078\u53d6\u4e26\uf967\u662f\u4e00\u500b\u9069</td></tr><tr><td>\u5408\u7684\u65b9\u6cd5\u3002</td><td/></tr><tr><td colspan=\"2\">\u57fa\u65bc\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u8a9e\u97f3\u5408\u6210\u5668\u662f\u4e00\u7a2e\u7d71\u8a08\u5f0f\uf96b\uf969\u8a9e\u97f3\u5408\u65b9\u6cd5\uff0c\u662f\u76ee\u524d\u6700\u70ba\u5ee3\u6cdb</td></tr><tr><td colspan=\"2\">\u63a1\u7528\u7684\u5408\u6210\u65b9\u6cd5\uff0c\u5b83\u4ee5\u6587\u8108\u76f8\u95dc\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(Context-dependent HMMs, CDHMMs)</td></tr><tr><td colspan=\"2\">\uf92d\u6a21\u64ec\uf967\u540c\u8a9e\u8a00\uf96b\uf969\u6216\u97fb\uf9d8\u67b6\u69cb\u4e0b\u7684\u8072\u5b78\u4fe1\u865f\uff0c\u5f9e\u8a9e\uf9be\u5eab\u8a13\uf996\u5f97\u5230\u983b\u8b5c\u6a21\u578b(spectral</td></tr><tr><td>parameter model)</td><td/></tr><tr><td/><td>) \u7684\u8a9e</td></tr></table>", | |
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| "html": null | |
| }, | |
| "TABREF4": { | |
| "text": "state \u4e4b mean vector \u53ca covariance matrix\u3002 label construction \u6b65\u9a5f\u5f8c \u7522\u751f\u6587\u672c\u6a19 \u793a (label) \uff0c\u4f9d\u64da\u6587\u672c\u6a19 \u793a\u4f7f\u7528\u4e09\u7a2e \u8a9e\u901f\u4e4b CDHMM \u6a21\u578b\u3001state duration \u6a21\u578b\u53ca CDHMM \u6a21\u578b\uf96b\uf969\u6b0a\u91cd\uff0c\u7531\u6587\u672c\u76f8\u95dc\u6c7a\u7b56\u6a39\u627e\u5230\u9069 \u7576\u7684\u6a21\u578b\uff0c\u9996\u5148\u9810\u4f30\u51fa\u6bcf\u500b\u8072\u6bcd\u3001\u97fb\u6bcd\u6216\u975c\u97f3\u505c\u9813\u7684\u9577\ufa01\uff0c\u518d\uf9dd\u7528 maximum likelihood \u6cd5 (Tokuda et al., 2000) \u7522\u751f\u6bcf\u500b\u97f3\u6846\u7684 logF0 \u53ca MGC \u983b\u8b5c\uf96b\uf969\u3002 \u70ba\u5be6\u9a57\u7d50\u679c\u3002 \u7531\u6574\u9ad4\uf92d\u770b Inside test \u7684 RMSE \u90fd\u4f4e\u65bc Outside test \u9019\u662f\u56e0\u70ba\u904e\ufa01\u8a13\uf996(overfitting) \u7684\u95dc\u4fc2\uff0c\u7d93\u89c0\u5bdf\u767c\u73fe\u975c\u97f3\u505c\u9813\u97f3\u9577\u7684\u9810\u6e2c\uf967\uf941 Outside test \u548c Inside test \u5728\u8a9e\u901f\u5feb\u7684 RMSE", | |
| "num": null, | |
| "content": "<table><tr><td>\u53ef\u8b8a\u901f\u4e2d\u6587\u6587\u5b57\u8f49\u8a9e\u97f3\u7cfb\u7d71</td><td>\u6c5f\u632f\u5b87 \u7b49 39</td></tr><tr><td colspan=\"2\">\u7531\u5be6\u9a57\u7d50\u679c\u767c\u73fe\uff0c\u5c0d\u65bc\u5feb\u901f\u5408\u6210\u8a9e\u97f3\u9810\u4f30\u975c\u97f3\u505c\u9813\u7684\u7d50\u679c\u662f\u6700\u5dee\u7684\uff0c\u932f\u8aa4\u5927\u591a\u662f\u5728 AR \u4ee5\u53ca\u57fa\u983b\u7684\u7d71\u8a08\u5dee\uf962\u4e26\uf967\u5927\uff0c\u56e0\u6b64\uf967\u8003\u616e 0.5-0.5-0 \u9019\u500b\u6b0a\u91cd\u503c\u7d44\u5408\uff0c\u6240\u4ee5\u672c\u5be6\u9a57\u53ea</td></tr><tr><td colspan=\"2\">\u9810\u6e2c\u76ee\u6a19\u8a9e\uf906\u6709\u975c\u97f3\u505c\u9813\u7684\u90e8\u4efd\uff0c\u4e3b\u8981\u539f\u56e0\u53ef\u80fd\u662f\u56e0\u70ba\u5feb\u901f\u8a9e\uf9be\uf9e8\u97f3\u7bc0\u9593\u7684\u975c\u97f3\u505c\u9813\u8f03 \u6709 16 \u7a2e\u975c\u97f3\u505c\u9813-CDHMM \u6b0a\u91cd\u7684\u7d44\u5408\u3002</td></tr><tr><td colspan=\"2\">\u5c11\uff0c\u6240\u4ee5\u9020\u6210\uf9ba\u6c7a\u7b56\u6a39\u5b78\u7fd2\u5230\u97f3\u7bc0\u9593\u7121\u975c\u97f3\u505c\u9813\u7684\u6a5f\uf961\u8f03\u5927\uff0c\u5728\u9810\u6e2c\u7d50\u679c\u4e5f\u662f\u504f\u5411\u6c92\u6709 \u6bcf\u4e00\u500b\u5408\u6210\u6587\u672c\u5747\u70ba outside test \u7684\u8a9e\uf906\uff0c\u4e00\u500b\u6587\u672c\u4f9d\u64da\uf978\u7d44\u6b0a\u91cd\u503c\u8b8a\u5316\u6703\u7522\u751f 16</td></tr><tr><td colspan=\"2\">\u7b2c\u4e00\u7d44\u6b0a\u91cd\u503c\u5f71\u97ff\u975c\u97f3\u505c\u9813\u7684\u8b8a\u5316\uff0c\u800c\u7b2c\u4e8c\u7d44\u6b0a\u91cd\u503c\u5f71\u97ff\uf9ba\u97f3\u9577\u3001\u983b\u8b5c\u53ca\u57fa\u983b\uff0c\u5728 \u81ea\u7136\u7684\u8a9e\u97f3\u8a0a\u865f\u4e2d\uff0c\u6162\u901f\u8a9e\uf9be\u975c\u97f3\u505c\u9813\u8f03\u591a\uff0c\u97f3\u7bc0\u97f3\u9577\u4e5f\u6703\uf925\u9577\uff0c\u5feb\u901f\u8a9e\uf9be\u5247\u76f8\u53cd\u3002\u5728 \u7d66\u5b9a\u6b0a\u91cd\u503c\u4e5f\u9700\u8981\u6309\u7167\u8a9e\u901f\u7684\u898f\u5247\uff0c\u7576\u60f3\u8981\u5408\u6210\u8a9e\u901f\u8f03\u5feb\u7684\u8a9e\u97f3\u8a0a\u865f\u6642\uff0c\u589e\u52a0\u5feb\u901f\u8a9e\u901f \u4e4b\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u7684\u6bd4\u91cd\uff0c\u4f7f\u975c\u97f3\u505c\u9813\u9810\u4f30\u51fa\u7684\uf969\uf97e\u8f03\u5c11\uff0c\u53ea\u5728\u9069\u5408\u7684\u4f4d\u7f6e\u7d66\u5b9a\u975c\u97f3\u505c \u9813\uff0c\u540c\u6642\uff0c\u6211\u5011\u4e5f\u8abf\u6574\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u6b0a\u91cd\uff0c\u589e\u52a0\u5feb\u901f\u8a9e\uf9be\u7684\u6bd4\u91cd\uff0c\u800c\u53ef\u4ee5\u7522\u751f\u51fa\u8f03 \u77ed\u7684\u97f3\u9577\u53ca\u8f03\u9ad8\u7684\u57fa\u983b\uff0c\u9019\uf978\u7d44\u6b0a\u91cd\u503c\u9700\u8981\u6709\u6b63\u76f8\u95dc\u624d\u6703\u5339\u914d\uff0c\uf978\u7d44\uf967\u5339\u914d\u7684\u6b0a\u91cd\u503c\u6703 \u5408\u6210\u51fa\uf967\u81ea\u7136\u7684\u8a9e\u97f3\u8a0a\u865f\uff0c\u56e0\u6b64\u5728\uf967\u540c\u8a9e\u901f\u4e0b\uf978\u7d44\u6b0a\u91cd\u503c\u7684\u5339\u914d\u662f\u76f8\u7576\u91cd\u8981\u7684\uff0c\u5728\u4e4b\u5f8c \u7684\u5be6\u9a57\u6703\u5c0d\u9019\uf978\u7d44\u6b0a\u91cd\u503c\u5339\u914d\u4f5c\u4e3b\u89c0\u6e2c\u8a66\u7684\u5be6\u9a57\u3002 3.3 Label Construction \u6b32\u5408\u6210\u7684\u6587\u5b57\u7d93\u7531\u6587\u672c\u5206\u6790\u5f8c\uff0c\u53ef\u5f97\u5230\u5c0d\u61c9\u6587\u8108\u76f8\u95dc\u7684\u8a9e\u8a00\uf96b\uf969\u8cc7\u8a0a\uff0c\u4f7f\u7528\u4e4b\u524d\u4ee5\u5167\u63d2 \u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u6240\u9810\u4f30\u4e4b\u975c\u97f3\u505c\u9813\uff0c\u653e\u5165\u6587\u672c\u6a19\u793a(label)\uff0c\u6700\u7d42\u7522\u751f\u7684 label \u5177\u6709\u6b32\u5408\u6210 \u6587\u672c\u4e2d\u6bcf\u500b\u8072\u6bcd\u3001\u97fb\u6bcd\u53ca\u975c\u97f3\u505c\u9813\u7684\u6587\u8108\u76f8\u95dc\u8a9e\u8a00\uf96b\uf969\u3002 Spectrum Approximation filter) (Imai, 1983)\u7522\u751f\u5408\u6210\u8a9e\u97f3\u3002 4. \u5be6\u9a57\u7d50\u679c\u53ca\u8a0e\uf941 \u5be6\u9a57\u8a9e\uf9be\u5df2\u65bc 1.4 \u4e2d\u4ecb\u7d39\uff0c\u5c0d\u65bc\u6bcf\u7a2e\u8a9e\u901f\u53d6\u5176\u7d04 328 \u500b\u8a9e\uf906\u70ba\u8a13\uf996\u8a9e\uf9be\uff0c\u53e6 20 \uf906\u70ba\u6e2c\u8a66 \u8a9e\uf9be\uff0c\u70ba\uf9ba\u8a55\u4f30\u672c\u53ef\u8b8a\u901f\u6f22\u8a9e\u8a9e\u97f3\u5408\u6210\u7cfb\u7d71\u7684\u6548\u80fd\uff0c\u6211\u5011\u5206\u5225\u5c0d\u5408\u6210\u8a9e\u97f3\u9032\ufa08\u5ba2\u89c0\u53ca\u4e3b \u89c0\u7684\u6e2c\u8a66\u3002\u5728\u5ba2\u89c0\u6e2c\u8a66\u65b9\u9762\uff0c\u6211\u5011\uf97e\u6e2c\uf9ba\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u7684\u9810\u4f30\u6b63\u78ba\u6027\uff0c\u53e6\u5916\uff0c\u4e5f\uf97e\u6e2c \uf9ba\u6574\u500b\u7cfb\u7d71\u5408\u6210\u8a9e\u97f3\u548c\u76ee\u6a19\u8a9e\u97f3\u7684\uf97e\u5316\u8aa4\u5dee\u3002\u800c\u5728\u4e3b\u89c0\u6e2c\u8a66\u65b9\u9762\uff0c\u5c0d\u7cfb\u7d71\uf978\u7d44\u6b0a\u91cd\u503c\u5339 \u914d\u7684\uf9fa\u6cc1\u4f5c\u4e3b\u89c0\u6e2c\u8a66\u7684\u5be6\u9a57\uff0c\u5408\u6210\u97f3\u6a94\u7684\u5c55\u793a\u8acb\uf99a\u7d50 http://140.113.144.71\u3002 4.1 \u5ba2\u89c0\u6e2c\u8a66 \u5728\u7b2c\u4e00\u500b\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u5c0d\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u7684\u6548\u80fd\u9032\ufa08\u8a55\u4f30\uff0c\u8a08\u7b97\u5408\u6210\u97f3\u6a94\u548c\u76ee\u6a19\u8a9e\uf906\u7684 \u975c\u97f3\u505c\u9813\u9810\u4f30\u7684\u6b63\u78ba\uf961\u53ca\u6df7\u6dc6\u7a0b\ufa01\uff0c\u56e0\u70ba\u53ea\u6709\u55ae\u7d14\u4e09\u7a2e\u8a9e\u901f\u7684\u76ee\u6a19\u8a9e\uf906\uff0c\u6c92\u6709\u5be6\u969b\u4ecb\u65bc \u9019\u4e09\u7a2e\u8a9e\u901f\u7684\u76ee\u6a19\u8a9e\uf906\uff0c\u6240\u4ee5\u53ea\u6709\u5c0d\u65bc\u4e09\u7a2e\uf967\u540c\u8a9e\u901f\u76ee\u6a19\u8a9e\uf906\u7684\u9810\u4f30\u7d50\u679c\u4f5c\u89c0\u5bdf\uff0c\u4ee5\u5408 \u6210\u5feb\u901f\u8a9e\u97f3\u70ba\uf9b5\uff0c\u7576\u6e2c\u8a66\u5feb\u901f\u8a9e\uf9be\u6642\uff0c\u6211\u5011\u8abf\u6574\u5feb\u901f\u7684\u975c\u97f3\u505c\u9813\u9810\u4f30\u6c7a\u7b56\u6a39\u6b0a\u91cd\u503c\u70ba 1\uff0c \u5176\u4ed6\u8a9e\u901f\u4e4b\u6b0a\u91cd\u70ba 0\uff0c\u4e2d\u6162\u901f\u6e2c\u8a66\u4ea6\u540c\u3002\u8868 4 \u70ba\u9810\u4f30\u975c\u97f3\u505c\u9813\u5c0d\u65bc\u5feb\u4e2d\u6162\u8a9e\u901f\u7684\u7d50\u679c\u3002 \u8868 4. \uf967\u540c\u8a9e\u901f\u4e0b\u9810\u4f30\u975c\u97f3\u505c\u9813\u7684\u7d50\u679c\uff0cXX*\u4ee3\u8868\u9810\u6e2c\u70ba\u975c\u97f3\u505c\u9813\u6216\u975e\u975c\u97f3\u505c\u9813 (\u4ee5 \u767e\u5206\u6bd4\u8868\u793a) \uff0cTotal \u70ba Non-SP \u6216 SP \u7684\u7e3d\u500b\uf969\u3002 \u6162 Inside Outside Non-SP* SP* Total Non-SP* SP* Total Non-SP 90.05 9.95 28108 Non-SP 89.66 10.34 1885 SP 30.19 69.81 20486 SP 33.57 66.43 1415 \u4e2d Inside Outside Non-SP* SP* Total Non-SP* SP* Total Non-SP 92.77 7.23 29119 Non-SP 91.55 8.45 1977 SP 37.81 62.19 19314 SP 39.61 60.39 1323 \u5feb Inside Outside Non-SP* SP* Total Non-SP* SP* Total \u975c\u97f3\u505c\u9813\uff0c\u53e6\u5916\u53ef\u80fd\u7684\u539f\u56e0\uff0c\u662f\u8003\u616e\u5230\u5feb\u901f\u8a9e\uf9be\uf967\uf941\u662f AR \u548c SR \u8b8a\u5316\u90fd\u662f\u6700\u5927\u7684\uff0c\u8a9e \uf906\u548c\u8a9e\uf906\u9593\u8a9e\u901f\u6709\u8f03\u5927\u7684\u5dee\uf962\uff0c\u56e0\u70ba\u8a9e\u901f\u548c\u975c\u97f3\u505c\u9813\u7684\u591a\u5be1\u6709\u95dc\u4fc2\uff0c\u8a9e\u901f\u7684\u5dee\uf962\u6f5b\u5728\u6703 \u9020\u6210\u5feb\u901f\u8a9e\uf9be\u975c\u97f3\u505c\u9813\u9810\u4f30\u4e0a\u7684\u56f0\u96e3\u3002\u5728\u6162\u901f\u8a9e\uf9be\u4e0a\u96d6\u7136\u5728\u975e\u975c\u97f3\u505c\u9813\u9810\u6e2c\u4e0a\uf976\u8f38\u5feb\u901f \u8a9e\uf9be\uff0c\u4f46\u5728\u6709\u975c\u97f3\u505c\u9813\u9810\u6e2c\u4e0a\u6bd4\u5feb\u901f\u8a9e\uf9be\u6e96\u5f97\u591a\uff0c\u53ef\u80fd\u662f\u56e0\u70ba\u8a9e\u8005\u65bc\uf929\uf95a\u6162\u901f\u8a9e\uf9be\u6642\uff0c \u6703\u5c07\u8a5e\u6216\u97fb\uf9d8\u8a5e\u7684\u7d50\u69cb\u6e05\u695a\uf9a3\u51fa\uff0c\u6240\u4ee5\u5728\u8a9e\uf9be\u4e0a\u7522\u751f\u8f03\u4e00\u81f4\u6027\u7684\u975c\u97f3\u505c\u9813\uff0c\u8f03\u5bb9\uf9e0\u5f9e\u8a9e \u8a00\uf96b\uf969\u5b78\u7fd2\u5230\u898f\u5247\uff0c\u56e0\u6b64\u6e96\u78ba\ufa01\u6bd4\u5feb\u901f\u8981\u9ad8\u7684\u591a\u3002 \u7b2c\u4e8c\u500b\u5ba2\u89c0\u6e2c\u8a66\uff0c\u6211\u5011\u5206\u5225\u6e2c\uf97e\u5408\u6210\u548c\u76ee\u6a19\u8a9e\uf906\u5176\u57fa\u983b\u3001\u505c\u9813\u975c\u97f3\u7684\u9577\ufa01\u4ee5\u53ca\u97f3\u7bc0 \u7684\u9577\ufa01\u7684\u8aa4\u5dee\uff0c\u4f7f\u7528\u5747\u65b9\u6839\u8aa4\u5dee(Root Mean Square Error, RMSE)\u7528\uf92d\u8a55\u4f30\u8aa4\u5dee\u503c\uff0c\u56e0 \u8a9e\uf9be\u5eab\u53ea\u6709\u4e09\u7a2e\u8a9e\u901f\uff0c\u6240\u4ee5\u5728\u6e2c\uf97e\u5feb\u901f\u8a9e\uf9be\u7684 RMSE \u6642\uff0c\u9810\u6e2c\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u7684\u6b0a\u91cd\u548c \u96b1\u85cf\u99ac\u53ef\u592b\u5f0f\u6a21\u578b\u7684\u6b0a\u91cd\uff0c\u5747\u8a2d\u5b9a\u5feb\u901f\u6b0a\u91cd\u503c\u70ba 1\uff0c\u5176\u4ed6\u6b0a\u91cd\u8a2d\u70ba 0\uff0c\u4e2d\u6162\u901f\u8a9e\uf9be\u4e5f\u4f7f\u7528 \u540c\u6a23\u7684\u65b9\u6cd5\u6e2c\uf97e\uff0c\u8868 5 \u5747\u70ba\u6700\u4f4e\uff0c\u7531\u65bc\u5feb\u901f\u8a9e\u901f\u5728\u975c\u97f3\u505c\u9813\u7684\u97f3\u9577\u4e26\uf967\u9577\uff0c\u5c31\u7b97\u975c\u97f3\u505c\u9813\u6c92\u6709\u6b63\u78ba\u9810\u4f30\u51fa\uf92d\uff0c \u8aa4\u5dee\u4e5f\uf967\u6703\u592a\u5927\uff0c\u800c\u6162\u901f\u7684\u975c\u97f3\u505c\u9813\u5c31\uf967\u4e00\u6a23\uff0c\u975c\u97f3\u505c\u9813\u97f3\u9577\u8f03\u9577\uff0c\u6c92\u6709\u6b63\u78ba\u9810\u4f30\u5230\u975c \u97f3\u505c\u9813\u8aa4\u5dee\u5c31\u6703\u8f03\u5927\uff0c\u6211\u5011\u89c0\u5bdf\u97f3\u7bc0\u97f3\u9577\u7684 RMSE \u4e5f\u770b\u5230\u540c\u6a23\u7684\u7d50\u679c\uff0c\u5728\u8a9e\u901f\u5feb\u7684\u97f3\u7bc0 \u97f3\u9577 RMSE \u5747\u70ba\u6700\u4f4e\uff0c\u56e0\u70ba\u5feb\u901f\u8a9e\uf9be\u97f3\u7bc0\u97f3\u9577\u90fd\u8f03\u77ed\uff0c\u8a08\u7b97\u8aa4\u5dee\u4e5f\uf967\u6703\u592a\u5927\u3002 \u8868 5. \u5feb\u4e2d\u6162\u8a9e\uf9be\u4f5c\u6e2c\u8a66\u4e4b RMSE \u503c \u6e2c\u8a66\u9805\u76ee \u8a9e\u901f Fast Median Slow Inside F0 (Hz) 36.28 34.38 35.21 Outside F0 (Hz) 42.66 42.78 45.23 Inside sp duration (ms) 44.97 64.19 84.17 Outside sp duration (ms) 56.55 60.02 85.55 Inside syllable duration (ms) 37.53 41.44 44.19 Outside syllable duration (ms) 39.23 42.66 47.08 4.2 \u4e3b\u89c0\u6e2c\u8a66 \u4e3b\u89c0\u6e2c\u8a66\u76ee\u7684\u70ba\u6e2c\u8a66\u7cfb\u7d71\uf978\u7d44\u6b0a\u91cd\u503c\uf967\u540c\u7684\u7d44\u5408\uff0c\u4ee5\u4e3b\u89c0\u6e2c\u8a66\u5224\u5225\u5408\u6210\u8a9e\u97f3\u7684\u81ea\u7136\ufa01\uff0c \u7a2e\uf967\u540c\u8a9e\u901f\u8b8a\u5316\u7684\u5408\u6210\u97f3\u6a94\uff0c\u5404\u5206\u70ba\u56db\u7d44\u4f5c\u6e2c\u8a66\uff0c\u4ee5\u540c\u6a23\u96b1\u85cf\u99ac\u53ef\u592b\u6a21\u578b\u7684\u6b0a\u91cd\u503c\u70ba\u540c \u4e00\u7d44\uff0c\u76ee\u7684\u70ba\u56fa\u5b9a\u4e00\u7d44\u6b0a\u91cd\u503c\uff0c\u89c0\u5bdf\uf967\u540c\u6b0a\u91cd\u9810\u6e2c\u975c\u97f3\u505c\u9813\u7684\u5339\u914d\u7a0b\ufa01\u3002\u4e3b\u89c0\u6e2c\u8a66\u4e2d\u8a9e \u97f3\u81ea\u7136\ufa01\u7684\u8a55\u5206\u70ba\u4e94\u5206\u5236\uff0c\u5206\uf969\u70ba\u4e00\u81f3\u4e94\uff0c\u4e00\u70ba\u6700\uf967\u81ea\u7136\uff0c\u4e94\u70ba\u6700\u81ea\u7136\uff0c\u7e3d\u5171\u5c0d 6 \u4eba\u4f5c \u4e3b\u89c0\u6e2c\u8a66\uff0c\u6bcf\u500b\u6e2c\u8a66\u8005\u7531\u4e5d\uf906\u6587\u672c\u4e2d\u9078\u807d\uf978\uf906\u6587\u672c\u7684\u8a9e\uf906\uff0c\u5176\u4e2d\u4e00\uf906\u6587\u672c\u8207\u53e6\u4e00\u500b\u6e2c\u8a66 \u8005\u91cd\u8907\uff0c\u56e0\u70ba\u6bcf\u6587\u672c\u6709 16 \u7a2e\u8a9e\u901f\u6b0a\u91cd\u7d44\u5408\uff0c\u6240\u4ee5\u6bcf\u500b\u4eba\u807d 32 \uf906\u6e2c\u8a66\u8a9e\uf906\uff0c\u6574\u500b\u6e2c\u8a66\u8a9e \uf906\u5171\u6709 192 \uf906\uff0c\u6e2c\u8a66\u7d50\u679c\u5982\u8868 6 \u6240\u793a\u3002 \u88686. \u4e3b\u89c0\u6e2c\u8a66\u7684\u5e73\u5747\u503c\u00b1\u4e00\u500b\u6a19\u6e96\u5dee\uff0cx-x-x\u4e2d\u7684x\u9806\u5e8f\u4ee3\u8868\u6162\u3001\u4e2d\u3001\u5feb\u6b0a\u91cd\u503c \u9810\u6e2c\u975c\u97f3\u505c\u9813\u6b0a\u91cd\u503c \u96b1\u85cf\u99ac\u53ef\u592b\u6b0a\u91cd\u503c 1-0-0 0-1-0 0-0.5-0.5 0-0-1 1-0-0 2.33\u00b10.61 3.08\u00b11.36 2.79\u00b10.98 2.21\u00b10.70 0-1-0 2.54\u00b10.88 3.38\u00b10.96 3.25\u00b10.391 2.21\u00b10.52 0-0.5-0.5 2.67\u00b10.60 3.08\u00b10.99 3.67\u00b10.79 2.54\u00b11.43 0-0-1 2.83\u00b10.88 2.88\u00b11.00 3.71\u00b10.93 3.25\u00b11.66 \u7531\u4e3b\u89c0\u5be6\u9a57\u767c\u73fe\uff0c\uf978\u7d44\u6b0a\u91cd\u503c\u5fc5\u9808\u6709\u6b63\u76f8\u95dc\u7684\u95dc\u4fc2\uff0c\u5408\u6210\u51fa\u7684\u8a9e\u97f3\u624d\u6703\u81ea\u7136\uff0c\u7576\uf978 \u7d44\u6b0a\u91cd\u503c\uf967\u76f8\u5339\u914d\u7684\u6642\u5019\uff0c\u5408\u6210\u51fa\u7684\u8a9e\u97f3\u5927\u591a\uf967\u81ea\u7136\uff0c\u56e0\u6b64\u901a\u5e38\u5728\u8868\uf9d1\u5c0d\u89d2\u7dda\u9644\u8fd1\u6703\u6709 \u6700\u5927\u7684\u81ea\u7136\ufa01\uff0c\u4f46\u7576\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u6b0a\u91cd\u503c\u70ba 0-0-1 \u548c\u96b1\u85cf\u99ac\u53ef\u592b\u6b0a\u91cd\u503c\u70ba 1-0-0 \u662f\u6bd4 \u8f03\uf9a8\u4eba\u8a1d\uf962\u7684\u7d50\u679c\uff0c\u731c\u6e2c\u5728\u96b1\u85cf\u99ac\u53ef\u592b\u6b0a\u91cd\u503c\u70ba 1-0-0 \u6642\u8a9e\u901f\u5f88\u6162\uff0c\u9020\u6210\u6e2c\u8a66\u8005\u807d\u5f97\u53ad \u7169\uff0c\u7531\u8868\uf9d1\u56fa\u5b9a\u96b1\u85cf\u99ac\u53ef\u592b\u6b0a\u91cd\u503c 1-0-0 \u89c0\u5bdf\uff0c\u767c\u73fe\u7121\uf941\u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u6b0a\u91cd\u5982\u4f55\u8abf\u6574\uff0c \u53d7\u6e2c\u8005\u6240\u7d66\u4e88\u7684\u81ea\u7136\ufa01\u90fd\u504f\u4f4e\uff0c\u5728\u9019\u7a2e\u6b0a\u91cd\u503c\u7d44\u5408\u4e0b\uff0c\u53d7\u6e2c\u8005\u89ba\u5f97\u53ad\u7169\u5206\uf969\u90fd\u7d66\u7684\u8f03\u4f4e\u3002 5. \u7d50\uf941\u8207\u672a\uf92d\u65b9\u5411 \u672c\u7cfb\u7d71\u70ba\u53ef\u8b8a\u901f\u4e2d\u6587\u6587\u5b57\u8f49\u8a9e\u97f3\uff0c\u7d93\u7531\u6b0a\u91cd\u503c\u8abf\u6574\u6240\u5408\u6210\u51fa\u7684\u8a9e\u97f3\uff0c\u5408\u6210\u51fa\uf92d\u7684\u8a9e\u97f3\u57fa \u672c\u4e0a\u5c1a\u4f73\uff0c\u5728\u4e3b\u89c0\u5be6\u9a57\u4e2d\uf978\u7d44\u6b0a\u91cd\u503c\u7686\u8abf\u70ba 0-0.5-0.5 \u6240\u5408\u6210\u51fa\u7684\u8a9e\u97f3\u81ea\u7136\ufa01\u4e5f\u662f\uf9a8\u4eba\u6eff \u610f\u7684\uff0c\u5176\u8a9e\u901f\u4ecb\u65bc\u4e2d\u901f\u53ca\u5feb\u901f\u4e4b\u9593\uff0c\u7cfb\u7d71\u53ef\u9810\u6e2c\u51fa\u9069\u7576\u7684\u975c\u97f3\u505c\u9813\u3001\u983b\u8b5c\u53ca\u5176\u4ed6\u97fb\uf9d8\uf96b \uf969\uff0c\u9054\u5230\u5408\u6210\u51fa\uf967\u540c\u8a9e\u901f\u7684\u81ea\u7136\u8a9e\u97f3\u3002\u7531\u5ba2\u89c0\u5be6\u9a57\u767c\u73fe\u5feb\u901f\u7684\u975c\u97f3\u505c\u9813\u9810\u4f30\u7d50\u679c\u8f03\u5dee\uff0c \u672a\uf92d\u7684\u7814\u7a76\u6703\u4ee5\u6162\u901f\u70ba\u57fa\u6e96\u9810\u4f30\u5176\u4ed6\u8a9e\u901f\u7684\u975c\u97f3\u505c\u9813\uff0c\u56e0\u70ba\u5728\u6162\u901f\u6642\uf95a\u7a3f\u4eba\u6703\u5b8c\u6574\u5206\u6790 \u8a5e\u548c\u97fb\uf9d8\u8a5e\u7d50\u69cb\u5f8c\uf9a3\u51fa\u8a9e\uf906\uff0c\u8003\u616e\u76f8\u5c0d\u975c\u97f3\u505c\u9813\u7684\u8b8a\u5316\uff0c\u5982\u67d0\u4e9b\u97f3\u7bc0\u9593\u6216\u8a5e\u9593\uf967\u7ba1\u5728\u6162 \u901f\u9084\u662f\u5feb\u901f\u90fd\u9700\u8981\u975c\u97f3\u505c\u9813\uff0c\u800c\u6709\u4e9b\u975c\u97f3\u505c\u9813\u5728\u5feb\u901f\u6642\u53cd\u800c\u6d88\u5931\uff0c\u8003\u616e\u9019\u4e9b\u76f8\u5c0d\u7684\u8b8a\u5316 \u518d\u9032\u4e00\u6b65\u9032\ufa08\u975c\u97f3\u505c\u9813\u9810\u4f30\u662f\u9700\u8981\u7684\u3002 3.4 \u5728 \u5c07\u4e0a\u4e00\u6b65\u5f97\u5230\u7684\u6bcf\u500b\u97f3\u6846\u4e4b logF0 \u548c MGC \u983b\u8b5c\uf96b\uf969\u8f38\u5165\u81f3 MSLA filter (Mel-Log Non-SP 96.34 3.66 35380 Non-SP 94.83 5.17 2496 \u5c0d\u65bc\u5feb\u4e2d\u6162\uf978\u7d44\u6b0a\u91cd\u503c\u8a2d\u70ba\uff1a1-0-0\u30010-1-0\u30010-0-1\u30010-0.5-0.5(x-x-x \u4e2d\u7684 x \u9806\u5e8f\u4ee3\u8868\u6162 \u975c\u97f3\u505c\u9813\u6c7a\u7b56\u6a39\u662f\u7531\u8a9e\u8a00\uf96b\uf969\u9810\u4f30\u8a5e\u4e4b\u9593\u975c\u97f3\u505c\u9813\u7684\u51fa\u73fe\u8207\u5426\uff0c\u96d6\u7136\u672c\u7cfb\u7d71\u6240\u9810\u4f30</td></tr><tr><td colspan=\"2\">SP \u901f\u3001\u4e2d\u901f\u3001\u4ee5\u53ca\u5feb\u901f\u6b0a\u91cd\u503c) \uff0c\u56e0\u70ba\u8003\u616e\u7684\u7d44\u5408\uf969\uf97e\u904e\u591a\uff0c\u800c\u4e14\u6162\u901f\u8ddf\u4e2d\u901f\u8a9e\uf9be\u4f9d\u64da SR\u3001 49.5 50.5 11613 SP 52.74 47.26 804 \u7684\u975c\u97f3\u505c\u9813\u7d50\u679c\u5c1a\u4f73\uff0c\u4f46\u4ee5\u9019\u500b\u7cfb\u7d71\u6240\u9810\u4f30\u51fa\uf92d\u975c\u97f3\u505c\u9813\u7279\u6027\u4e26\u6c92\u6709\u8003\u616e\u5be6\u969b\u975c\u97f3\u505c\u9813</td></tr></table>", | |
| "type_str": "table", | |
| "html": null | |
| } | |
| } | |
| } | |
| } |