Base_JSON/prefixO/json/O13/O13-1018.json

{
    "paper_id": "O13-1018",
    "header": {
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        "date_generated": "2023-01-19T08:04:04.863877Z"
    },
    "title": "Multilingual Acoustic Model Splitting and Merging by Latent Dirichlet Allocation",
    "authors": [
        {
            "first": "Jui-Feng",
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            "first": "",
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            "last": "Yeh",
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        {
            "first": "Sheng-Feng",
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            "last": "\u674e\u52dd\u8c50",
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            "affiliation": {},
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        {
            "first": "",
            "middle": [],
            "last": "Li",
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        {
            "first": "Shi-Sheng",
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            "last": "\u8a31\u5e0c\u8056",
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            "first": "",
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            "last": "Shiu",
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        {
            "first": "David",
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                "M"
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            "first": "",
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            "last": "Blei",
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    "year": "",
    "venue": null,
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    "abstract": "To avoid the confusion of phonetic acoustic models between different languages is one of the most challenges in multilingual speech recognition. We proposed the method based on Latent Dirichlet Allocation to avoid the confusion of phonetic acoustic models between different languages. We split phonetic acoustic models based on tri-phone. And merging the group that selected by Latent Dirichlet Allocation Detector to solve pronunciation variants problems between different languages. This paper has three parts. First part is introduced the Pronunciation Event and Articulatory Features. Second part is about Latent Dirichlet Allocation and the acoustic model selecting method using Latent Dirichlet Allocation. Latent Dirichlet Allocation is a Hierarchical math model that proposed by David M. Blei at 2003. It is often used on documents classification and document generation. The structure of LDA is also suitable for speech recognition and nature language processing. The final is experiment result and verification the method we proposed.",
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        "paper_id": "O13-1018",
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        "abstract": [
            {
                "text": "To avoid the confusion of phonetic acoustic models between different languages is one of the most challenges in multilingual speech recognition. We proposed the method based on Latent Dirichlet Allocation to avoid the confusion of phonetic acoustic models between different languages. We split phonetic acoustic models based on tri-phone. And merging the group that selected by Latent Dirichlet Allocation Detector to solve pronunciation variants problems between different languages. This paper has three parts. First part is introduced the Pronunciation Event and Articulatory Features. Second part is about Latent Dirichlet Allocation and the acoustic model selecting method using Latent Dirichlet Allocation. Latent Dirichlet Allocation is a Hierarchical math model that proposed by David M. Blei at 2003. It is often used on documents classification and document generation. The structure of LDA is also suitable for speech recognition and nature language processing. The final is experiment result and verification the method we proposed.",
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                "section": "Abstract",
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                "sec_num": null
            },
            {
                "text": ", \u03b2) = \u222c p(\u03b8 M |\u03b1 M ) p(\u03b8 P |\u03b1 P ) {\u220f p(Z M |\u03b8 M )p(Z P |\u03b8 P )p(w|Z M , Z P , L, \u03b2) N n=1 } d\u03b8 M d\u03b8 P (\u5f0f 4.1) \u5716\u4e8c\u3001\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5075\u6e2c\u5668\u4e4b\u5716\u578b\u8868\u793a\u6cd5 \u8868\u4e00\u3001\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5075\u6e2c\u5668\u4e4b\u53c3\u6578\u4ee3\u8868\u610f\u7fa9 \u7b26\u865f \u63cf\u8ff0 \u03b1M K \u5411\u91cf\uff0c\u767c\u97f3\u65b9\u6cd5\u7684 dirichlet \u5206\u5e03 \u03b1P K \u5411\u91cf\uff0c\u767c\u97f3\u90e8\u4f4d\u7684 dirichlet",
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\u5f8c\u6574\u5408\u5728\u4e00\u8d77\u8a2d\u8a08\u4e00\u500b\u8fa8\u8b58\u5668\u53ef\u4ee5\u76f4\u63a5\u8fa8\u8b58\u591a\u570b\u8a9e\u8a00\u7684\u67b6\u69cb\u5247\u88ab\u63a1\u7528\uff0c\u4ee5\u6e1b\u5c11\u932f\u8aa4\u7387\u7684 \u7d2f\u52a0\u3002\u8981\u5c07\u591a\u500b\u8a9e\u8a00\u6574\u5408\u5728\u540c\u4e00\u8fa8\u8b58\u5668\u5167\u5927\u81f4\u4e0a\u53ef\u5206\u70ba\u4e09\u7a2e\u65b9\u6cd5\uff0c\u7b2c\u4e00\u7a2e\u662f\u5c07\u5404\u500b\u55ae\u4e00 \u8a9e\u8a00\u7684\u97f3\u6a19\u96c6\u5408\u4f75\u6210\u5171\u540c\u97f3\u6a19\u96c6\u5408\uff0c\u4e26\u4ee5\u6b64\u70ba\u4f9d\u64da\u4f86\u5efa\u7acb\u591a\u8a9e\u7684\u8fa8\u8b58\u5668\u3002\u7b2c\u4e8c\u7a2e\u662f\u4f7f\u7528 \u5c08\u5bb6\u77e5\u8b58\u6240\u5efa\u7acb\u7684\u8de8\u8a9e\u8a00\u97f3\u6a19\u96c6\u5408\u4f86\u5408\u4f75\u4e0d\u540c\u8a9e\u8a00\u7684\u97f3\u6a19\uff0c\u76ee\u524d\u570b\u969b\u97f3\u6a19\u96c6\u5408\u6709\u570b\u969b\u97f3 \u4e9b\u4e0d\u540c\u7684\u60c5\u6cc1\u3002\u6216\u8005\u662f\u4e0d\u540c\u8a9e\u8a00\u7684\u4e0d\u540c\u7684\u97f3\u6a19\uff0c\u4f46\u662f\u5176\u767c\u97f3\u6a21\u5f0f\u537b\u6975\u70ba\u76f8\u4f3c\u3002\u70ba\u4e86\u89e3\u6c7a \u4e0a\u8ff0\u4e4b\u554f\u984c\uff0c\u56e0\u6b64\u5c07\u540c\u97f3\u6a19\u4f46\u767c\u97f3\u4e0d\u540c\u7684\u8072\u5b78\u6a21\u578b\u9032\u884c\u62c6\u5206\uff0c\u800c\u97f3\u6a19\u4e0d\u540c\u767c\u97f3\u985e\u4f3c\u7684\u8072 \u5b78\u6a21\u578b\u9032\u884c\u5408\u4f75\uff0c\u4ee5\u6e1b\u5c11\u6df7\u6dc6\u7684\u60c5\u6cc1\u3002 (\u4e8c)\u76f8\u95dc\u7814\u7a76 Haizhou Li, Bin Ma, and Chin-Hui Lee \u65bc\u671f\u520a\u4e0a\u767c\u8868\u7684\u7814\u7a76\u591a\u8a9e\u8fa8\u8b58[7]\uff0c\u63d0\u51fa\u4e86\u65b0 \u7684\u8fa8\u8b58\u55ae\u5143\u69cb\u60f3\uff0c\u4e0d\u518d\u4ee5\u97f3\u6a19\u70ba\u55ae\u5143\u800c\u662f\u7528\u4eba\u985e\u5be6\u969b\u767c\u97f3\u7684\u65b9\u6cd5\u4f86\u505a\u70ba\u55ae\u4f4d\u3002\u6b64\u65b9\u6cd5\u662f \u5f9e\u8a0a\u865f\u9762\u5c0b\u627e\u5404\u7a2e\u7279\u5fb5\uff0c\u4e26\u4ee5\u9019\u4e9b\u7279\u5fb5\u5efa\u7acb\u51fa\u5404\u7a2e\u767c\u97f3\u4e8b\u4ef6(Pronunciation Event)\u7684\u8fa8\u8b58 \u5668\uff0c\u4e26\u5c07\u6240\u6709\u767c\u97f3\u4e8b\u4ef6\u8fa8\u8b58\u5668\u7d50\u679c\u9032\u884c\u4ea4\u53c9\u6bd4\u5c0d\u51fa\u6240\u8981\u8fa8\u8b58\u55ae\u5143\u4e4b\u97f3\u6a19\uff0c\u6b64\u65b9\u6cd5\u53ef\u4ee5\u6709 \u6548\u6e1b\u4f4e\u8072\u5b78\u6a21\u578b\u6578\u91cf\uff0c\u56e0\u6b64\u5177\u6709\u8f03\u4f73\u7684\u6297\u566a\u80fd\u529b[8]\u3002 \u738b\u5c0f\u5ddd[9]\u6559\u6388\u5728\"\u8a9e\u97f3\u4fe1\u865f\u8655\u7406\"\u4e00\u66f8\u4e2d\u5c0d\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u57fa\u790e\u77e5\u8b58\u80cc\u666f\u548c\u6d41\u7a0b\u6709\u76f8 \u7576\u8a73\u7d30\u7684\u6558\u8ff0\uff0c\u5c0d\u65bc\u60f3\u8981\u5b78\u7fd2\u8a9e\u97f3\u8fa8\u8b58\u7684\u4eba\u662f\u4e00\u672c\u5f88\u597d\u7684\u5165\u9580\u66f8\u7c4d\u3002\u9577\u5e9a\u5927\u5b78\u5247\u662f\u9577\u5e74 \u4f86\u9032\u884c\u53f0\u7063\u672c\u571f\u8a9e\u8a00\u7684\u7814\u7a76\uff0c\u6881\u654f\u96c4[10]\u5efa\u7acb\u4ee5 IPA \u70ba\u57fa\u790e\u7684\u53f0\u8a9e\u97f3\u6a19\u96c6\u5408 ForPA\u3001\u6587 \u5b57\u8f49\u8a9e\u97f3(TTS)\u548c\u767c\u97f3\u5075\u932f\u61c9\u7528\u65bc\u8a9e\u8a00\u6559\u5b78\u8207\u53f0\u8a9e\u8a9e\u97f3\u8a9e\uf9be\u5eab\u8a2d\u8a08\u8207\u6536\u96c6\u3002\u9673\u5fd7\u5b87[11]\u5247 \u63a2\u8a0e\u4e86\u540c\u6642\u5c0d\u570b\u53f0\u96d9\u8a9e\u7684\u5927\u8a5e\u5f59\u9023\u7e8c\u8a9e\u97f3\u8fa8\u8b58\u3002\u694a\u6c38\u6cf0[12]\u5c07\u97f3\u6a19\u66ff\u63db\u6539\u8b8a\u6210\u4e2d\u6587\u800c\u5c07 \u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u904b\u7528\u5728\u4e2d\u6587\u8fa8\u8b58\u4e0a\u3002 \u975e\u672c\u570b\u6bcd\u8a9e\u4eba\u58eb\u6240\u8b1b\u7684\u8a9e\u8a00\u6703\u7522\u751f\u767c\u97f3\u8b8a\u7570\uff0c\u4f8b\u5982\u53f0\u7063\u4eba\u8b1b\u82f1\u6587\uff0c\u6b64\u7a2e\u767c\u97f3\u8b8a\u7570\u6703 \u4f7f\u7cfb\u7d71\u7522\u751f\u6975\u5927\u7684\u8aa4\u5224\u3002\u570b\u7acb\u6210\u529f\u5927\u5b78\u7684\u8521\u4f69\u73ca\u3001\uf972\u6db5\u5e73\u3001\u5433\u5b97\u61b2[13]\u63d0\u51fa\u4ee5\u66f4\u5c0f\u7684\u55ae \u4f4d \u2500 senone \u4f5c\u70ba\u57fa\u672c\u7684\u8fa8\u8b58\u55ae\u5143\uff0c\u4ee5\u66f4\u52a0\u8a73\u7d30\u7684\u6a21\u64ec\u767c\u97f3\u8b8a\u7570\uff0c\u4e26\u5efa\u7acb\u5305\u542b\u767c\u97f3\u8b8a\u7570 \u4e4b\u82f1\u6587\u8072\u5b78\u6a21\u578b\u3002 \u4e8c\u3001\u7cfb\u7d71\u67b6\u69cb (\u4e00)\u7cfb\u7d71\u6846\u67b6 \u5716\u4e00\u70ba\u672c\u8ad6\u6587\u4e4b\u7cfb\u7d71\u67b6\u69cb\u3002\u6839\u64da\u5176\u8655\u7406\u6d41\u7a0b\u53ef\u4ee5\u5206\u70ba\u8a13\u7df4\u968e\u6bb5\u8207\u6e2c\u8a66\u968e\u6bb5\u3002\u8a13\u7df4\u968e \u6bb5\u70ba\u4f7f\u7528\u72c4\u5f0f\u5206\u4f48\u9032\u884c\u5c0d\u97f3\u7d20\u8072\u5b78\u6a21\u578b\u7684\u6df7\u6dc6\u5075\u6e2c\uff0c\u6e2c\u8a66\u968e\u6bb5\u5247\u662f\u5c07\u6df7\u6dc6\u767c\u751f\u7684\u6a21\u578b\u9032 \u884c\u5206\u5272\u6216\u5408\u4f75\u5f8c\u9032\u884c\u6548\u80fd\u6e2c\u8a66\u3002 \u5716\u4e00\u3001\u7cfb\u7d71\u67b6\u69cb (\u4e8c)\u8a13\u7df4\u90e8\u5206 \u5728\u8a13\u7df4\u6642\u7684\u521d\u59cb\u968e\u6bb5\u70ba\u4f7f\u7528 39 \u7dad MFCC \u53c3\u6578\u548c HTK \u4f86\u5efa\u7acb\u8d77\u4ee5\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21 \u578b(HMM, Hidden Markov Model)\u70ba\u57fa\u790e\u7684\u591a\u8a9e\u8a00 Tri-phone \u8072\u5b78\u6a21\u578b\u3002\u6b64\u968e\u6bb5\u5c07\u4e0d\u540c\u8a9e \u8a00\u76f8\u540c\u97f3\u6a19\u8996\u70ba\u4e0d\u540c\u97f3\u6a19\uff0c\u4e26\u4e14\u6839\u64da Tri-phone \u5b9a\u7fa9\u5206\u88c2\u6a21\u578b\uff0c\u4f46\u662f\u4e0d\u9032\u884c State-Tying \u548c\u6a21\u578b\u5408\u4f75\u3002\u7b2c\u4e8c\u968e\u6bb5\u70ba\u4f7f\u7528\u8a9e\u6599\u6a19\u8a18\u5efa\u7acb\u4ee5\u72c4\u5f0f\u5206\u4f48(LDA, Latent Dirichlet Allocation) \u70ba\u57fa\u790e\u7684\u5075\u6e2c\u5668\uff1a\u7fa4\u5167\u9a57\u8b49\u5075\u6e2c\u5668(Intra-clustering verification Detector)\u3002\u904e\u4f86\u6839\u64da\u4ee5\u72c4 \u5f0f\u5206\u4f48\u70ba\u57fa\u790e\u7684\u7fa4\u5167\u9a57\u8b49\u5075\u6e2c\u5668\u5206\u5225\u6839\u64da\u767c\u97f3\u90e8\u4f4d\u548c\u767c\u97f3\u65b9\u6cd5\u9032\u884c\u5206\u7fa4\uff0c\u7531\u65bc\u6bcf\u4e00\u500b\u97f3 \u7d20\u90fd\u53ef\u4ee5\u5c0d\u61c9\u7684\u55ae\u4e00\u7684\u767c\u97f3\u90e8\u4f4d\u548c\u767c\u97f3\u65b9\u6cd5\uff0c\u56e0\u6b64\u5c07\u767c\u97f3\u65b9\u6cd5\u8207\u767c\u97f3\u90e8\u4f4d\u7686\u5206\u985e\u5728\u540c\u4e00 (\u4e09)\u6e2c\u8a66\u90e8\u5206 \u6e2c\u8a66\u90e8\u5206\u6700\u4e3b\u8981\u7684\u662f\u5c0d\u8a13\u7df4\u968e\u6bb5\u6240\u4fee\u6b63\u7684\u8072\u5b78\u6a21\u578b\u9032\u884c\u6548\u80fd\u8207\u6b63\u78ba\u6027\u6e2c\u8a66\u3002\u5f9e\u4f7f\u7528 \u8005\u8f38\u5165\u7684\u8a9e\u53e5\u62bd\u53d6 39 \u7dad MFCC \u53c3\u6578\u548c\u8a9e\u97f3\u5c6c\u6027\u4e26\u4f7f\u7528\u4fee\u6b63\u904e\u7684\u8072\u5b78\u6a21\u578b\u9032\u884c\u8fa8\u8b58\uff0c\u4e26 \u6839\u64da\u8fa8\u8b58\u7d50\u679c\u4f86\u5224\u65b7\u662f\u5426\u6709\u6e1b\u5c11\u8072\u5b78\u6a21\u578b\u6df7\u6dc6\u7a0b\u5ea6\u3002 \u4e09\u3001\u767c\u97f3\u4e8b\u4ef6 \u6a19(\u865f\u96c6\uff0c\u4f46\u5728\u4e0d\u540c\u8a9e\u8a00\u7684\u60c5\u6cc1\u4e0b\uff0c\u6703\u51fa\u73fe\u96d6\u7136\u5c6c\u65bc\u540c\u4e00\u500b\u97f3\u6a19\uff0c\u4f46\u662f\u5176\u767c\u97f3\u6a21\u5f0f\u4ecd\u7136\u6703\u6709 \u70ba\u4e86\u89e3\u6c7a\u767c\u97f3\u8b8a\u7570\u800c\u9020\u6210\u8a9e\u97f3\u6a21\u578b\u6df7\u6dc6\u7684\u60c5\u5f62\uff0c\u570b\u5916\u5b78\u8005\u6839\u64da\u4e0d\u540c\u7684\u767c\u97f3\u8b8a\u7570\u6cd5\u9032 \u70ba\u4e86\u89e3\u6c7a\u8a9e\u97f3\u8fa8\u8b58\u7684\u74f6\u9838\uff0c\u7f8e\u570b\u55ac\u6cbb\u4e9e\u7406\u5de5\u5b78\u9662\u7684\u674e\u9326\u8f1d(C.-H. Lee)\u6559\u6388\u63d0\u51fa\u4e86\u5075 \u884c\u4e86\u8a31\u591a\u7684\u7814\u7a76\u3002\u95dc\u65bc\u500b\u4eba\u5316\u767c\u97f3\u8b8a\u7570\u7684\u7814\u7a76\uff0c1993 \u5e74 Hamada \u548c Miki \u7b49\u4eba\u63d0\u51fa\uff0c\u904b \u6e2c\u5f0f(Detection-based)\u7684\u65b9\u6cd5\uff0c\u5176\u4e3b\u8981\u6982\u5ff5\u70ba\u4eba\u985e\u518d\u767c\u97f3\u7684\u6642\u5019\u6703\u6709\u767c\u97f3\u90e8\u4f4d(Place)\u8207\u767c \u7528\u52d5\u614b\u898f\u5283(dynamic programming)\u548c\u5411\uf97e\uf97e\u5316(vector quantization)\u7684\u65b9\u5f0f\u6bd4\u8f03 Native \u97f3\u65b9\u6cd5(Manner)\uff0c\u85c9\u7531\u767c\u97f3\u8a9e\u8a00\u5b78\u4f86\u63cf\u8ff0\u8a9e\u97f3\u3002\u800c\u767c\u97f3\u90e8\u4f4d\u8207\u767c\u97f3\u65b9\u6cd5\u5247\u7d71\u7a31\u70ba\u767c\u97f3\u4e8b Speaker \u8207 Non-Native Speaker \u5728\u540c\u4e00\u500b\u5b57\u767c\u97f3\u4e0a\u7684\u5dee\u7570[2] \u30021996 \u5e74 Neumeyer \u548c Franc \u4ef6\u3002\u800c\u70ba\u4e86\u8981\u5075\u6e2c\u767c\u97f3\u4e8b\u4ef6\uff0c\u5247\u85c9\u7531\u76f4\u63a5\u89c0\u5bdf\u8a9e\u97f3\u8a0a\u865f\u4f86\u5c0b\u627e\u51fa\u7279\u5b9a\u767c\u97f3\u4e8b\u4ef6\u4e0b\u7684\u6709\u6548 \u7b49\u4eba\u5247\u5b9a\u7fa9\u4e86 HMM Log-Likelihood\u3001Segment duration \u548c Timing \u7b49\u7279\u5fb5\u53c3\u6578\uff0c\u91dd\u5c0d\u6574 \u7279\u5fb5\u4f86\u5075\u6e2c\uff0c\u800c\u9019\u4e9b\u7279\u5fb5\u5247\u7a31\u70ba\u8a9e\u97f3\u5c6c\u6027(Articulatory Features)\u3002\u5176\u7279\u6027\u662f\u85c9\u7531\u591a\u5c64\u5316\u67b6 \u500b\u53e5\u5b50\u505a\u767c\u97f3\u8a55\u4f30\uff0c\u800c\u4e14\u5be6\u9a57\u7684\u7d50\u679c\u767c\u73fe normalized segment duration scores \u8207\u5c08\u5bb6\u7d66 \u69cb\u800c\u7e2e\u6e1b\u4e86\u6a21\u578b\u6578\u91cf\uff0c\u4e5f\u56e0\u70ba\u8207\u4eba\u985e\u767c\u97f3\u7684\u65b9\u6cd5\u6709\u6240\u95dc\u9023\uff0c\u56e0\u6b64\u5176\u5177\u6709\u8f03\u9ad8\u7684\u5f37\u5065\u6027\u3002 \u56e0\u6b64\u5c0d\u65bc\u4e0d\u540c\u8a9e\u8a00\u76f8\u540c\u97f3\u6a19\u4e5f\u8f03\u4e0d\u5bb9\u6613\u7522\u751f\u6df7\u6dc6\u3002\u70ba\u4e86\u8655\u7406\u5728\u591a\u8a9e\u8fa8\u8b58\u7684\u74b0\u5883\u4e0b\u7684\u97f3\u6a19 \u6df7\u6dc6\uff0c\u672c\u6587\u4f7f\u7528\u4e86\u767c\u97f3\u4e8b\u4ef6\u4f86\u9032\u884c\u5075\u6e2c\u3002 \u767c\u97f3\u4e8b\u4ef6\u6709\u8a31\u591a\u4e0d\u540c\u7684\u5206\u985e\u6cd5\uff0c\u4f46\u5176\u5171\u901a\u7279\u6027\u5247\u70ba\u53ef\u4ee5\u8de8\u8a9e\u8a00\u7684\u5c0d\u61c9\u5230\u7279\u5b9a\u7684\u97f3\u6a19\uff0c \u4e88\u7684\u5206\u6578\u4e2d\u6709\u6700\u9ad8\u7684\u76f8\u95dc\u6027[3]\u30021997 \uf98e Final State Transducer (FST) [6]\u3002 \u56e0\u6b64\u70ba\u4e86\u5efa\u7acb\u8d77\u8de8\u8a9e\u8a00\u7684\u97f3\u6a19\u96c6\u5408\uff0c\u672c\u6587\u4e3b\u8981\u4f7f\u7528\u4e86\u570b\u969b\u97f3\u6a19\u96c6\u5408</td></tr><tr><td>\u7fa4\u4e4b\u97f3\u7d20\u5b9a\u70ba\u5408\u4f75\u76ee\u6a19\uff0c\u800c\u5c07\u6c92\u6709\u5169\u8005\u7686\u5206\u985e\u5728\u540c\u4e00\u7fa4\u7684\u97f3\u6a19\u793a\u70ba\u4e0d\u540c\u4e4b\u5206\u7fa4\u3002\u6700\u5f8c\u518d</td></tr><tr><td>\u6839\u64da\u6b64\u5206\u7fa4\u5c0d\u7b2c\u4e00\u968e\u6bb5\u6240\u5efa\u7acb\u7684\u8072\u5b78\u6a21\u578b\u9032\u884c\u5408\u4f75\uff0c\u6700\u5f8c\u5373\u53ef\u5f97\u5230\u4fee\u6b63\u904e\u4e4b\u8072\u5b78\u6a21\u578b\u3002</td></tr></table>",
                "text": "The International Phonetic Alphabet, IPA)\u3001\u5b57\u6bcd\u97f3\u6a19\u8a55\u4f30\u6cd5(Speech Assessment Methods Phonetic Alphabet, SAMPA)\u3001Worldbet \u7b49\u3002\u7b2c\u4e09\u7a2e\u662f\u8a08\u7b97\u4e0d\u540c\u97f3\u6a19\u4e4b\u9593\u7684\u76f8\u4f3c\u6027\uff0c\u4e26\u7531 \u4f30\u7b97\u51fa\u4f86\u7684\u76f8\u4f3c\u6027\u4f86\u5efa\u7acb\u5171\u540c\u7684\u97f3\u6a19\u96c6\u3002 \u672c\u6587\u4e3b\u8981\u6ce8\u91cd\u65bc\u4f7f\u7528\u5c08\u5bb6\u77e5\u8b58\u6240\u5efa\u7acb\u7684\u8de8\u8a9e\u8a00\u97f3\u6a19\u96c6\u5408\u4e4b\u65b9\u6cd5\uff0c\u5728\u6b64\u4e3b\u8981\u662f\u4f7f\u7528 \u570b\u969b\u97f3\u6a19(The International Phonetic Alphabet, IPA)\uff0c\u96d6\u7136 IPA \u63d0\u4f9b\u4e86\u4e00\u500b\u901a\u7528\u7684\u97f3\u6a19\u7b26 Ronen \u7b49\u4eba\u63d0\u51fa MisPronunciation network \u7684\u6982 \uf9a3\u3002\u8003\u616e\u6bcf\u500b\u97f3\u7d20\u5728 native speaker \u8207 non-native speaker \u7684\u767c\u97f3\u60c5\u5f62\uff0c\u5efa\uf9f7\u5c0d\u61c9\u7684 HMM Model\uff0c\u8fa8\u8b58\u7684\u6642\u5019\u767c\u97f3\u7db2\uf937\u540c\u6642\u8003\u616e native speaker \u8207 non-native speaker \u7684\u767c\u97f3\u60c5\u5f62 [4]\u30021999 \uf98e Franco \u7b49\u5247\u662f\u4f7f\u7528\u5169\u500b\u5206\u5225\u7531 native speaker \u8207 non-native speaker \u6240\u8a13\uf996 \u7684\u8072\u5b78\u6a21\u578b\uff0c\u5229\u7528 log-likelihood ratio \u4f86\u8a55\u4f30\u767c\u97f3\u932f\u8aa4\uff0c\u540c\u6642\u4e5f\u8b49\u660e\uf9ba\u9019\u6a23\u7684\u65b9\u6cd5\u6bd4\u5229 \u7528 a posterior score \u7684\u65b9\u5f0f\u8207\u5c08\u5bb6\u6240\u7d66\u4e88\u7684\u5206\u6578\u6709\uf901\u9ad8\u7684\u76f8\u95dc\u6027[5]\u3002\u5728\u6a21\u578b\u65b9\u9762\uff0c\u5247\u662f \u9ebb\u7701\u7406\u5de5\u5b78\u9662\u7684",
                "type_str": "table"
            },
            "TABREF1": {
                "html": null,
                "num": null,
                "content": "<table><tr><td>(\u4e8c)\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u6a21\u578b(Latent Dirichlet Allocation Model)</td></tr><tr><td>1. \u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48(Latent Dirichlet Allocation)</td></tr><tr><td>\u6f5b \u85cf \u72c4\u5f0f\u5206\u4f48(Latent Dirichlet Allocation, LDA) \u662f \u7531 \u6a5f \u7387 \u5f0f \u6f5b \u85cf \u8a9e \u610f \u5206 \u6790</td></tr><tr><td>(Probabilistic Latent Semantic Analysis, PLSA)\u5ef6\u4f38\u767c\u5c55\u800c\u4f86\uff0c\u800c\u6a5f\u7387\u5f0f\u6f5b\u85cf\u8a9e\u610f\u5206\u6790\u53c8</td></tr><tr><td>\u662f\u7531\u6f5b\u85cf\u8a9e\u610f\u5206\u6790(Latent Semantic Analysis, LSA)\u767c\u5c55\u800c\u4f86\u3002\u4e0a\u8ff0\u4e09\u500b\u6a21\u578b\u90fd\u662f\u5c6c\u65bc\u6f5b</td></tr><tr><td>\u85cf\u4e3b\u984c\u6a21\u578b(Latent Topic Model)\uff0c\u7531\u65bc N \u9023\u6a21\u578b\u6240\u9762\u81e8\u7684\u7f3a\u4e4f\u9577\u8ddd\u96e2\u8cc7\u8a0a\u548c\u8cc7\u6599\u7a00\u758f\u554f</td></tr><tr><td>\u984c\uff0c\u800c\u6f5b\u85cf\u4e3b\u984c\u6a21\u578b\u5247\u662f\u89e3\u6c7a\u554f\u984c\u591a\u7a2e\u65b9\u6cd5\u7684\u5176\u4e2d\u4e4b\u4e00\u3002\u5176\u6982\u5ff5\u70ba\u4f7f\u7528\u975e\u76e3\u7763\u5f0f\u5b78\u7fd2\u65b9</td></tr><tr><td>\u6cd5(Unsupervised Training)\u4f86\u627e\u51fa\u96b1\u542b\u65bc\u6587\u4ef6\u6216\u6587\u7ae0\u4e2d\u7684\u6700\u4e3b\u8981\u7684\u4e3b\u984c\u8a9e\u610f\u8cc7\u8a0a\u3002</td></tr><tr><td>\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u4e0d\u540c\u65bc\u6a5f\u7387\u5f0f\u6f5b\u85cf\u8a9e\u610f\u5206\u6790\u7684\u5730\u65b9\u5728\u65bc\u5f9e\u6587\u4ef6\u5230\u4e3b\u984c\u4e4b\u9593\u591a\u4e86\u4e00\u5c64</td></tr><tr><td>\u7684\u72c4\u5f0f\u5206\u5e03(Dirichlet Distribution)\uff0c\u4f7f\u5f97\u6a21\u578b\u53c3\u6578\u6578\u91cf\u4e0d\u6703\u56e0\u70ba\u8a9e\u6599\u589e\u52a0\u800c\u5927\u5e45\u5ea6\u7684\u589e</td></tr><tr><td>\u52a0\uff0c\u540c\u6642\u5c0d\u65bc\u51fa\u73fe\u5728\u8a9e\u6599\u5eab\u5916\u7684\u6587\u4ef6\u4e5f\u53ef\u4ee5\u5f9e\u72c4\u5f0f\u5206\u4f48\u4e2d\u53d6\u51fa\u4e00\u500b\u6700\u9069\u5408\u6b64\u6587\u4ef6\u7684\u6f5b\u85cf</td></tr><tr><td>\u4e3b\u984c\u6a5f\u7387\u5206\u4f48\u3002\u7531\u65bc\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u662f\u85c9\u7531\u9006\u5411\u901a\u904e\u6587\u4ef6\u5efa\u7acb\u751f\u6210\u6a21\u578b\uff0c\u56e0\u6b64\u8981\u5148\u7406\u89e3\u6f5b</td></tr><tr><td>\u85cf\u72c4\u5f0f\u5206\u4f48\u662f\u5982\u4f55\u7522\u751f\u4e00\u7bc7\u6587\u7ae0\u7684\u3002</td></tr><tr><td>\u5047\u8a2d\u8a9e\u6599\u5eab M 2.\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5075\u6e2c\u5668</td></tr><tr><td>\u96d6\u7136\u5075\u6e2c\u5f0f\u65b9\u6cd5(Detection Based)\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u5c0d\u65bc\u4e0d\u540c\u8a9e\u8a00\u540c\u97f3\u6a19\u4e4b\u767c\u97f3\u4ecd\u7136\u5177</td></tr><tr><td>\u6709\u9ad8\u5f37\u5065\u6027\u4e0d\u81f3\u65bc\u6df7\u6dc6\uff0c\u4f46\u662f\u5f80\u5f80\u4e0d\u540c\u8a9e\u8a00\u7684\u8a9e\u8005\u5c0d\u65bc\u76f8\u540c\u97f3\u6a19\u4e4b\u767c\u97f3\u8b8a\u7570\u4ecd\u7136\u6703\u4f7f\u5f97</td></tr><tr><td>\u767c\u97f3\u4e8b\u4ef6\u7522\u751f\u6df7\u6dc6\uff0c\u6709\u9452\u65bc\u6b64\u73fe\u8c61\u96e3\u4ee5\u4f7f\u7528\u77ed\u8ddd\u96e2\u7684\u8a0a\u865f\u4f86\u89e3\u6c7a\uff0c\u56e0\u6b64\u6211\u5011\u4f7f\u7528\u9577\u8ddd\u96e2</td></tr><tr><td>\u8a5e\u5f59\u8a9e\u610f\u8cc7\u8a0a\u4f86\u5354\u52a9\u5075\u6e2c\u51fa\u6df7\u6dc6\u7684\u554f\u984c\u3002\u4e26\u5c07\u6703\u767c\u8072\u6df7\u6dc6\u7684\u8072\u5b78\u6a21\u578b\u4ee5\u53ca\u6027\u8cea\u76f8\u540c\u7684\u8072</td></tr><tr><td>\u5b78\u6a21\u578b\u9032\u884c\u5408\u4f75\u3002\u4e0d\u540c\u65bc\u4e09\u9023\u97f3\u7d20\u6a21\u578b(Tri-Phone)\u7684\u72c0\u614b\u805a\u985e(State-Tying)\u6839\u64da\u77ed\u8ddd\u96e2\u7684 2003 \u5e74\u6240 \u8cc7\u6599\u76f8\u4f3c\u5ea6\u5408\u4f75\uff0c\u6211\u5011\u85c9\u7531\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u64f7\u53d6\u9577\u8ddd\u96e2\u8a5e\u5f59\u8a9e\u610f\u8cc7\u8a0a\uff0c\u4f8b\u5982\u89f8\u767c\u8a5e\u5c0d\uff0c\u4ee5</td></tr><tr><td>\u63d0\u51fa\u7684\uff0c\u6700\u521d\u662f\u7528\u65bc\u6587\u7ae0\u548c\u6587\u672c\u7684\u4e3b\u984c\u5075\u6e2c\u3002\u72c4\u5f0f\u5206\u4f48\u6a21\u578b\u6709\u4e00\u500b\u5148\u6c7a\u689d\u4ef6\uff1a\u8a5e\u888b\u5047\u8a2d \u53ca\u4f7f\u7528\u4e86\u767c\u97f3\u8a9e\u8a00\u5b78\u7684\u5b9a\u7fa9\u5c0d\u5206\u88c2\u7684\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u9032\u884c\u8072\u5b78\u6a21\u578b\u7684\u5408\u4f75\u3002</td></tr><tr><td>(Bag of Words Assumption)\uff0c\u4e5f\u5c31\u662f\u4e0d\u8003\u616e\u8a5e\u5f59(Word)\u5728\u6587\u7ae0\u4e2d\u7684\u51fa\u73fe\u9806\u5e8f\u548c\u6587\u6cd5\u95dc\u4fc2\uff0c</td></tr><tr><td>\u53ea\u8003\u616e\u55ae\u4e00\u8a5e\u5f59\u5728\u7279\u5b9a\u6587\u7ae0\u4e2d\u4e4b\u51fa\u73fe\u6b21\u6578\u3002\u5176\u6240\u4f7f\u7528\u4e4b\u539f\u7406\u70ba\u67d0\u4e9b\u8a5e\u5f59\u5728\u7279\u5b9a\u4e3b\u984c\u4e0b\u51fa \u7531\u65bc\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5177\u6709\u8a5e\u888b\u5047\u8a2d\u7684\u524d\u63d0\u4e0b\uff0c\u5176\u6240\u89c0\u5bdf\u5230\u7684\u8cc7\u6599\u9577\u5ea6\u8db3\u5920\u5305\u542b\u9577\u8ddd\u96e2\u7684\u8a5e</td></tr><tr><td>\u73fe\u4e4b\u6a5f\u7387\u548c\u6b21\u6578\u8f03\u9ad8\uff0c\u56e0\u6b64\u72c4\u5f0f\u5206\u4f48\u7684\u6a21\u578b\u5efa\u7acb\u65b9\u5f0f\u70ba\u7d71\u8a08\u5404\u500b\u8a5e\u5f59(Word)\u5728\u6587\u672c\u4e2d\u51fa \u5f59\u8a9e\u610f\u8cc7\u8a0a\u3002\u56e0\u70ba\u8a9e\u97f3\u4e8b\u4ef6\u548c\u8a9e\u97f3\u5c6c\u6027\u7684\u76f8\u4e92\u95dc\u4fc2\u4e5f\u662f\u5c6c\u65bc\u968e\u5c64\u5316\u67b6\u69cb\uff0c\u56e0\u6b64\u6839\u64da\u5176\u8072</td></tr><tr><td>\u73fe\u7684\u6b21\u6578\u4f86\u8a08\u7b97\u3002\u7531\u65bc\u6b64\u65b9\u6cd5\u5728\u6587\u672c\u548c\u6587\u7ae0\u5206\u985e\u53ef\u4ee5\u53d6\u5f97\u76f8\u7576\u597d\u7684\u6210\u6548\uff0c\u56e0\u6b64\u88ab\u5ee3\u6cdb\u7684 \u5b78\u6a21\u578b\u7684\u76f8\u4e92\u95dc\u4fc2\u5c0d\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u4e4b\u67b6\u69cb\u91cd\u65b0\u5efa\u69cb\u5f8c\u4e4b\u5716\u578b\u8868\u793a\u6cd5\u5982\u5716\u4e8c \uff0c\u5716\u5f62\u4e2d\u53c3</td></tr><tr><td>\u4f7f\u7528\u65bc\u4e3b\u984c\u5075\u6e2c\u548c\u4e3b\u984c\u5206\u985e\u7684\u61c9\u7528\u4e0a\u3002 \u6578\u6240\u4ee3\u8868\u4e4b\u610f\u7fa9\u5982\u8868\u4e00\u3002</td></tr><tr><td>\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\uff0c\u56e0\u70ba\u4e0d\u540c\u8a9e\u8a00\u7684\u8a9e\u8005\u9593\u4e4b\u767c\u97f3\u8b8a\u5316\u5e45\u5ea6\u6703\u76f8\u7576\u7684\u5927\uff0c\u4e5f\u56e0\u6b64\u82e5\u662f\u6839 \u5728\u9019\u88e1\u5c07\u6bcf\u4e00\u6bb5\u8a9e\u53e5(Utterance)\u8996\u70ba\u4e00\u500b\u6587\u4ef6(Document)\uff0c\u56e0\u6b64\u6839\u64da\u6f5b\u85cf\u5f0f\u72c4\u5f0f\u5206</td></tr><tr><td>\u64da\u4ee5\u5f80\u7684\u8072\u5b78\u6a21\u578b\u53ea\u4f7f\u7528\u77ed\u671f\u5167(Short-term)\u7684\u8cc7\u6599\u4f86\u8fa8\u8b58\u6240\u80fd\u63d0\u5347\u7684\u6548\u80fd\u5e45\u5ea6\u6709\u9650\uff0c\u800c \u4f48\u5b9a\u7fa9\u5247\u53ef\u4ee5\u5f97\u5230\u7279\u5b9a\u767c\u97f3\u4e8b\u4ef6\u8207\u8a9e\u610f\u4e0a\u7684\u76f8\u5c0d\u95dc\u4fc2\u3002\u800c\u806f\u5408\u6a5f\u7387\u5206\u4f48\u70ba</td></tr><tr><td>\u4e14\u5728\u767c\u97f3\u8b8a\u7570\u7522\u751f\u7684\u72c0\u6cc1\u4e0b\u4e5f\u96e3\u4ee5\u53ea\u4f7f\u7528\u8a0a\u865f\u4e0a\u77ed\u671f\u7684\u8cc7\u6599\u4f86\u52a0\u4ee5\u8b58\u5225\u3002\u4f46\u8a9e\u97f3\u8a0a\u865f\u7684 p(M|\u03b1 M , \u03b1 P</td></tr><tr><td>\u6642\u8b8a\u6027\u76f8\u7576\u7684\u5927\uff0c\u56e0\u6b64\u5728\u8a0a\u865f\u9762\u4e00\u6b21\u4f7f\u7528\u8f03\u9577\u8cc7\u6599\u7684\u65b9\u6cd5\u96e3\u5ea6\u4e5f\u9ad8\u3002\u6240\u4ee5\u672c\u6587\u6839\u64da\u8a9e\u6599</td></tr><tr><td>\u6a19\u8a18\u4f7f\u7528\u72c4\u5f0f\u5206\u4f48\u5c07\u6574\u53e5\u7684\u8cc7\u8a0a\u4e5f\u4e00\u4f75\u8003\u616e\u4ee5\u63d0\u5347\u5c0d\u767c\u97f3\u8b8a\u7570\u4e4b\u5f37\u5065\u6027\u3002</td></tr></table>",
                "text": "\u4e2d\u6709 K \u500b\u4e3b\u984c\uff0cT1,T2,T3,\u2026.Tk\uff0c\u4e26\u4e14\u6709 V \u500b\u5b57\u5f59\uff0c\u7576\u96a8\u6a5f\u9078\u53d6\u4e00\u500b \u4e3b\u984c Ti \u7684\u6642\u5019\uff0c\u4ee5 Ti \u70ba\u4e3b\u984c\u7684\u6587\u7ae0\u5247\u6709\u4e00\u7684\u5e8f\u5217\u6587\u5b57\uff0c\u800c\u9019\u4e9b\u6587\u5b57\u8207 Ti \u6709\u95dc\u9023\uff0c\u4e26\u4e14 \u6709\u4e00\u500b\u6a5f\u7387\u503c\u4ee3\u8868\u5728\u4e3b\u984c Ti \u4e0b\u6642\u6bcf\u500b\u6587\u5b57\u6240\u51fa\u73fe\u4e4b\u6a5f\u7387\u3002\u800c\u9078\u64c7\u5176\u4ed6\u4e3b\u984c\u6642\u5f8c\u4e5f\u6703\u6709 \u76f8\u540c\u7684\u53c3\u6578\u4f86\u63cf\u8ff0\u8a72\u4e3b\u984c\u3002\u6b64\u6642\u9650\u5b9a\u6587\u6a94\u9577\u5ea6\u70ba N\uff0c\u4e0d\u505c\u7684\u6311\u9078\u6587\u5b57\u76f4\u5230\u6578\u91cf\u5230 N \u5f8c\uff0c \u4fbf\u53ef\u4ee5\u85c9\u7531\u5c0d\u61c9\u7684\u53c3\u6578\u5f97\u5230\u8a72\u5efa\u7acb\u6587\u4ef6\u5c0d\u65bc\u5404\u4e3b\u984c\u7684\u76f8\u95dc\u6027\u3002",
                "type_str": "table"
            },
            "TABREF2": {
                "html": null,
                "num": null,
                "content": "<table><tr><td>\u5206\u5e03 \u67d0\u6bb5\u8a9e\u53e5\u7684\u767c\u97f3\u65b9\u6cd5\u767c\u751f\u4e4b\u6a5f\u7387 \u6240\u6709\u8a9e\u8a00\u4e0b\u4e4b\u8072\u5b78\u6a21\u578b\u6a5f\u7387 \u67d0\u6bb5\u8a9e\u53e5\u7684\u767c\u97f3\u90e8\u4f4d\u767c\u751f\u4e4b\u6a5f\u7387 \u8072\u5b78\u6a21\u578b(phone model) \u8a9e\u6599\u5eab \u67d0\u6bb5\u8a9e\u53e5\u7684\u8072\u5b78\u6a21\u578b\u96c6\u5408 \u8a9e\u8a00 \u767c\u97f3\u90e8\u4f4d(Place) \u767c\u97f3\u65b9\u6cd5(Manner) \u6839\u64da\u5f0f 4.1 \u8981\u8a08\u7b97\u7684\u53c3\u6578\u70ba \u03b1M, \u03b1P ,\u03b2\uff0c\u4f46\u7531\u65bc\u7528\u4f86\u4f30\u7b97\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u53c3\u6578\u7684\u6700\u5927\u671f\u671b\u6f14 \u03b2 \u03b8M \u03b8P W M N L ZP ZM \u7b97\u6cd5(Expectation-maximization algorithm, EM algorithm)\u4e00\u6b21\u53ea\u80fd\u4f30\u6e2c\u5169\u500b\u53c3\u6578\uff0c\u800c\u6839\u64da \u767c\u97f3\u8a9e\u8a00\u5b78\u4e4b\u5b9a\u7fa9\u767c\u97f3\u65b9\u6cd5\u548c\u767c\u97f3\u90e8\u4f4d\u5169\u4e8b\u4ef6\u70ba\u4e92\u76f8\u7368\u7acb(independent)\uff0c\u56e0\u6b64\u6211\u5011\u53ef\u4ee5 \u5c07\u5176\u62c6\u6210 \u03b1M\u3001\u03b2 \u548c \u03b1P \u3001\u03b2 \u5169\u500b\u90e8\u5206\u5206\u958b\u4f30\u6e2c\u3002 \u800c\u62c6\u958b\u5f8c\u5206\u5225\u7684\u806f\u5408\u6a5f\u7387\u5247\u5206\u5225\u70ba\u70ba\u5f0f 4.2 \u8207\u5f0f 4.3 \u6240\u793a p(M|\u03b1 M , \u03b2) = \u222b p(\u03b8 M |\u03b1 M ) {\u220f p(Z M |\u03b8 M )p(w|Z M , L, \u03b2) N n=1 } d\u03b8 M (\u5f0f 4.2) p(M|\u03b1 P , \u03b2) = \u222b p(\u03b8 P |\u03b1 P ) {\u220f p(Z P |\u03b8 P )p(w|Z P , L, \u03b2) N n=1 } d\u03b8 P (\u5f0f 4.3) \u4e4b\u5f8c\u6211\u5011\u53ef\u4ee5\u7531 \u03b2 \u5f97\u5230\u6bcf\u500b\u4e3b\u984c\u4e0b\u6240\u6709\u97f3\u7d20\u7684\u51fa\u73fe\u6a5f\u7387\uff0c\u800c\u9032\u800c\u5f97\u5230\u5206\u985e\u904e\u5f8c\u7684\u97f3\u7d20\u96c6 \u5408\u3002\u5728\u539f\u59cb\u7684\u72c4\u5f0f\u5206\u4f48\u4e2d\u6703\u767c\u751f\u82e5\u55ae\u4e00\u6587\u4ef6\u904e\u77ed\u6703\u56e0\u70ba\u6709\u6548\u8cc7\u8a0a\u904e\u5c11\u800c\u4f7f\u5f97\u6a21\u578b\u8a13\u7df4\u904e \u7a0b\u7121\u6cd5\u6536\u6582\u6216\u8005\u662f\u5f71\u97ff\u5230\u7d50\u679c\u3002\u4f46\u5728\u6211\u5011\u7684\u7814\u7a76\u4e2d\uff0c\u8a9e\u53e5\u4e2d\u7684\u6bcf\u4e00\u500b\u97f3\u7d20\u5c0d\u61c9\u5230\u6f5b\u85cf\u72c4 \u5f0f\u5206\u4f48\u90fd\u53ef\u4ee5\u8996\u70ba\u5177\u6709\u8cc7\u8a0a\u7684\u55ae\u8a5e\uff0c\u56e0\u6b64\u5373\u4f7f\u53ea\u6709 2-4 \u500b\u5b57\u8a5e\u7684\u77ed\u53e5\u7684\u8a9e\u53e5\u4e5f\u5177\u6709\u8db3\u5920 \u7684\u8cc7\u8a0a\u4f86\u52a0\u4ee5\u5224\u5b9a\u5176\u7d44\u6210\u3002 (\u4e09)\u8a9e\u97f3\u4e8b\u4ef6\u964d\u7dad\u8207\u5408\u4f75\u8072\u5b78\u6a21\u578b\u4e4b\u9078\u64c7 1.\u8a9e\u97f3\u4e8b\u4ef6\u964d\u7dad \u5728\u672c\u6587\u4e2d\u5c07\u4e00\u6bb5\u8a9e\u53e5\u8996\u70ba\u4e00\u500b\u6587\u4ef6\uff0c\u56e0\u6b64\u6839\u64da\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u4e4b\u7269\u7406\u610f\u7fa9\uff1a\u82e5\u67d0\u500b \u5b57\u8a5e(Word)\u5f9e\u5c6c\u65bc\u67d0\u500b\u4e3b\u984c\uff0c\u5247\u7576\u67d0\u6587\u4ef6\u5c6c\u65bc\u67d0\u500b\u7279\u5b9a\u4e3b\u984c\u7684\u6642\u5019\uff0c\u5247\u5f9e\u5c6c\u65bc\u67d0\u500b\u4e3b\u984c \u7684\u5b57\u8a5e\u51fa\u73fe\u6b21\u6578\u6703\u8f03\u9ad8\u3002\u5c07\u4e3b\u984c\u5c0d\u61c9\u5230\u7684\u5247\u662f\u5b57\u8a5e(Word)\u3002\u56e0\u70ba\u5728\u7279\u5b9a\u7684\u8a9e\u8a00\u4e0b\uff0c\u82e5\u4ee5 \u5b57\u8a5e\u70ba\u55ae\u4f4d\uff0c\u6709\u4e9b\u97f3\u7d20\u7d93\u5e38\u6027\u7684\u6703\u51fa\u73fe\u5728\u4e00\u8d77\uff0c\u56e0\u6b64\u672c\u6587\u5c07\u539f\u72c4\u5f0f\u5206\u4f48\u4e4b\u4e3b\u984c\u6578\u8a2d\u5b9a\u70ba \u5b57\u8a5e\u6578\u91cf\u3002 \u4f46\u7531\u65bc\u5b57\u8a5e\u6578\u91cf\u904e\u591a\uff0c\u800c\u4e14\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u4e4b\u904b\u7b97\u6642\u9593\u8207\u4e3b\u984c\u6578 N \u6210\u6b63\u6bd4\uff0c\u82e5\u5c07\u6240 \u6709\u53ef\u80fd\u51fa\u73fe\u7684\u5b57\u8a5e\u6578\u91cf\u8a2d\u5b9a\u70ba\u4e3b\u984c\u6578\uff0c\u5176\u4e3b\u984c\u6578\u904e\u591a\u4e0d\u50c5\u5728\u904b\u7b97\u6642\u9593\u4e0a\u4e0d\u5141\u8a31\uff0c\u4e5f\u56e0\u70ba \u76ee\u6a19\u4e4b\u96c6\u5408\u904e\u5927\uff0c\u8a9e\u6599\u6703\u9762\u81e8\u56b4\u91cd\u4e0d\u8db3\u4e4b\u60c5\u6cc1\uff0c\u56e0\u6b64\u5be6\u969b\u4e0a\u4e26\u4e0d\u53ef\u884c\u3002\u56e0\u6b64\u70ba\u4e86\u89e3\u6c7a\u6b64 \u554f\u984c\uff0c\u6211\u5011\u5c07\u5b57\u7684\u7d44\u6210\u97f3\u7d20\u6839\u64da\u570b\u969b\u97f3\u6a19\u96c6\u5408\u4e4b\u5b9a\u7fa9\uff0c\u5c07\u6bcf\u500b\u97f3\u7d20\u62c6\u89e3\u6210\u767c\u97f3\u65b9\u6cd5 (Manner)\u8207\u767c\u97f3\u4f4d\u7f6e(Place)\uff0c\u800c\u767c\u97f3\u65b9\u6cd5\u548c\u767c\u97f3\u4f4d\u7f6e\u5728\u9019\u88e1\u5373\u662f\u8a9e\u97f3\u4e8b\u4ef6\u3002 2.\u5408\u4f75\u8072\u5b78\u6a21\u578b\u4e4b\u9078\u64c7 \u672c\u6587\u6839\u64da\u570b\u969b\u97f3\u6a19\u96c6\u5408\u5b9a\u7fa9\u5c07\u767c\u97f3\u90e8\u4f4d(Place)\u5206\u70ba 13 \u985e\uff0c\u800c\u767c\u97f3\u65b9\u6cd5(Manner)\u5b9a \u70ba 6 \u985e\uff0c\u800c\u6bcd\u97f3\u5247\u7531\u820c\u9762\u524d\u5f8c\u5171 5 \u985e\u3001\u820c\u9762\u9ad8\u4f4e\u5171 7 \u985e\u3001\u5507\u5f62\u5171 2 \u985e\u3002\u820c\u9762\u524d\u5f8c\u70ba\u524d (Front)\u3001\u6b21\u524d(Near-front)\u3001\u592e (Central)\u3001\u6b21\u5f8c(Near-back)\u3001\u5f8c (Back)\u3002\u820c\u9762\u9ad8\u4f4e\u70ba\u9589(Close)\u3001 \u6b21\u9589(Near-close) \u3001\u534a\u9589(Close-mid) \u3001\u4e2d (Mid) \u3001\u534a\u958b(Open-mid) \u3001\u6b21\u958b(Near-open) \u3001\u958b (Open) \u3002 \u8123\u5f62\u70ba\u5713\u5507(Rounded)\u8207\u975e\u5713\u5507(Unrounded)\u3002 \u6211\u5011\u6703\u5148\u5c07\u767c\u97f3\u65b9\u6cd5\u548c\u767c\u97f3\u90e8\u4f4d\u4f7f\u7528\u4e0d\u540c\u7684\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5075\u6e2c\u5668\u5206\u5225\u5075\u6e2c\u4e26\u5206\u7fa4\uff0c \u6700\u5f8c\u518d\u5c07\u767c\u97f3\u90e8\u4f4d\u76f8\u540c\u4f46\u767c\u97f3\u65b9\u6cd5\u4e0d\u540c\u4ee5\u53ca\u767c\u97f3\u90e8\u4f4d\u4e0d\u540c\u4f46\u767c\u97f3\u65b9\u6cd5\u76f8\u540c\u7684\u97f3\u7d20\u8996\u70ba \u4e0d\u540c\u5206\u7fa4\uff0c\u63db\u53e5\u8a71\u8aaa\u5247\u662f\u53ea\u7559\u4e0b\u767c\u97f3\u90e8\u4f4d\u8207\u767c\u97f3\u65b9\u6cd5\u7686\u5206\u985e\u518d\u4e00\u8d77\u7684\u97f3\u7d20\u9032\u884c\u5408\u4f75\u3002 \u4e94\u3001\u5be6\u9a57\u8a2d\u8a08\u8207\u5206\u6790 (\u4e00) \u5be6\u9a57\u8a9e\u6599\u8207\u5de5\u5177 \u53f0\u8a9e\u5be6\u9a57\u8a9e\u6599\u4f7f\u7528\u826f\u654f\u96c4\u535a\u58eb\u6240\u9304\u88fd\u4e4b\u8a9e\u6599\uff0c\u8a9e\u6599\u70ba 16kHz, 16bit \u4e4b\u9ea5\u514b\u98a8\u8a9e\u6599\u5171 \u6bcf\u6b21\u4f4d\u79fb\u55ae\u4f4d(Shift)\u70ba 10ms\u3002\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u6bcf\u500b\u72c0\u614b(State)\u7559\u4e0b 16 \u500b\u8def\u5f91\uff0c\u6700\u5f8c \u518d\u53d6\u524d 10 \u540d\u3002\u6240\u6709\u8a9e\u6599\u7686\u7121\u6642\u9593\u6a19\u8a18\uff0c\u6642\u9593\u65b7\u9ede\u4f7f\u7528 Baum-Welch algorithms \u8a08\u7b97\u3002 (\u4e8c)\u5be6\u9a57\u9805\u76ee 1.\u8a55\u4f30\u65b9\u5f0f \u8981\u5206\u6790\u8a9e\u97f3\u8fa8\u8b58\u7684\u6b63\u78ba\u7387\u5fc5\u9808\u6e96\u78ba\u7684\u8fa8\u8b58\u51fa\u6b63\u78ba\u7684\u5b57\u8a5e\u3002\u8fa8\u8b58\u7d50\u679c\u8207\u8a9e\u6599\u6a19\u8a18\u4e92 \u76f8\u6bd4\u8f03\u5f8c\uff0c\u7d50\u679c\u8207\u6a19\u8a18\u5b8c\u5168\u76f8\u7b26\u70ba\u8fa8\u8b58\u6b63\u78ba(Correct)\uff0c\u8207\u6a19\u8a18\u4e0d\u540c\u7684\u932f\u8aa4\u5247\u6839\u64da\u5b9a\u7fa9\u5206 \u985e\u6210\u4e0b\u5217\u5e7e\u7a2e\u932f\u8aa4\uff1a \u5176\u4e2d N \u70ba\u8a9e\u6599\u6a19\u8a18\u5167\u6240\u6709\u7684\u97f3\u7d20\u7e3d\u6578\uff0cS \u70ba\u53d6\u4ee3\u932f\u8aa4\u6578\u91cf\uff0cD \u70ba\u522a\u9664\u932f\u8aa4\u6578\u91cf\uff0cI \u70ba\u63d2 \u5165\u932f\u8aa4\u6578\u91cf\uff0cC \u70ba\u8fa8\u8b58\u6b63\u78ba\u5b57\u8a5e\u6578\u91cf\uff0cC=N-D-S\u3002 2.\u591a\u8a9e\u74b0\u5883\u4e0b\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5408\u4f75\u8072\u5b78\u6a21\u578b\u9078\u64c7\u4e4b\u9a57\u8b49 \u70ba\u4e86\u9a57\u8b49\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5408\u4f75\u8072\u5b78\u6a21\u578b\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u4e4b\u6548\u80fd\uff0c\u56e0\u6b64\u6211\u5011\u540c\u6a23\u5206\u5225\u5c0d\u672a \u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u3001\u5df2\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u548c\u7531\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5408\u4f75\u8072\u5b78\u6a21\u578b\u5728\u570b\u53f0\u82f1\u4e09\u8a9e 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\u8ff0\u8a55\u4f30\u65b9\u5f0f\u6240\u9032\u884c\u7684\u5be6\u9a57\u4f7f\u7528\u76f8\u540c\u7684\u6e2c\u8a66\u8a9e\u6599\u3002\u8a9e\u6599\u5167\u55ae\u4e00\u8a9e\u53e5\u53ea\u6709\u4e00\u7a2e\u8a9e\u8a00\uff0c\u4e5f\u5c31\u662f \u4e0d\u505a\u55ae\u4e00\u8a9e\u53e5\u591a\u7a2e\u8a9e\u8a00\u6df7\u5408\u4e4b\u5be6\u9a57\u3002\u6240\u6709\u8a9e\u8a00\u7684\u97f3\u7d20\u7686\u4ee5 IPA \u4f86\u8868\u793a\u3002 (1)\u570b\u53f0\u82f1\u4e09\u8a9e\u6df7\u5408\u5be6\u9a57 \u7d50\u679c\u5982\u5716\u4e09\u6240\u793a\uff0c\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5408\u4f75\u7684\u8072\u5b78\u6a21\u578b\u5728 Top5 \u6642\u5019\u6709 47.86%\u7684\u6700\u9ad8\u6e96\u78ba\u7387\uff0c\u4e14\u6574\u9ad4\u6e96\u78ba\u7387\u7686\u9ad8\u65bc\u5176\u4ed6\u5169\u7a2e\u8072\u5b78\u6a21\u578b\u3002\u5728 Top5 \u6642\u7684\u63d2\u5165\u932f\u8aa4\uff0c \u672c\u6587\u63d0\u51fa\u4e4b\u65b9\u6cd5\u8f03\u5df2\u805a\u5408\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u591a 0.24%\uff0c\u4f46\u53d6\u4ee3\u932f\u8aa4\u548c\u522a\u9664\u932f\u8aa4\u5247\u8f03\u5df2\u805a\u5408\u4e09 \u9023\u97f3\u7d20\u6a21\u578b\u5206\u5225\u5c11\u4e86 1.02%\u548c 0.74%\uff0c\u56e0\u6b64\u6574\u9ad4\u7684\u6e96\u78ba\u7387\u8f03\u5df2\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u591a\u4e86 1.52%\u3002 (2)\u570b\u8a9e\u5be6\u9a57 \u5982\u5716\u56db\u6240\u793a\uff0c\u672a\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u8fa8\u8b58\u570b\u8a9e\u6709\u6700\u9ad8\u7684\u6e96\u78ba\u7387\u3002\u800c\u6709\u7d93 \u904e\u6a21\u578b\u5408\u4f75\u7684\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u548c\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u9078\u64c7\u8072\u5b78\u6a21\u578b\u64c7\u662f\u6b63\u78ba\u7387\u5dee\u8ddd\u4e0d\u5927\uff0c\u4f46 \u5169\u8005\u7686\u4f4e\u65bc\u672a\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u3002\u800c\u672a\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u932f\u8aa4\u7387\u6539\u5584\u4e3b\u8981\u96c6\u4e2d\u5728\u53d6\u4ee3\u932f \u8aa4\u3002\u6f5b\u5728\u72c4\u5f0f\u5206\u4f48\u548c\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u7684\u6bd4\u8f03\u5728\u63d2\u5165\u932f\u8aa4\u7565\u9ad8\u800c\u522a\u9664\u932f\u8aa4\u4f4e\u5247\u662f\u8207\u672c\u6587 \u524d\u5217\u55ae\u8a9e\u74b0\u5883\u4e0b\u4e4b\u5be6\u9a57\u76f8\u540c\u3002\u56e0\u6b64\u63a8\u6e2c\u6709\u53ef\u80fd\u662f\u5728\u9032\u884c\u591a\u8a9e\u6a21\u578b\u5408\u4f75\u6642\u5f8c\u570b\u8a9e\u7684\u97f3\u6a19\u5408 \u4f75\u4e0a\u51fa\u73fe\u554f\u984c\u6216\u8005\u662f\u56e0\u70ba\u8a13\u7df4\u8a9e\u6599\u904e\u5c11\u800c\u5c0e\u81f4\u6a21\u578b\u63cf\u8ff0\u4e0d\u5920\u5168\u9762\uff0c\u56e0\u6b64\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u7522 \u751f\u4e86\u56b4\u91cd\u7684\u6a21\u578b\u6df7\u6dc6\u3002 42.88% 45.06% 46.94% 48.08% 48.99% 38.37% 40.75% 42.42% 43.47% 44.04% 38.95% 40.64% 42.07% 43.23% 44.44% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% Top1 Top2 Top3 Top4 Top5 Accuracy Triphone Tied Triphone LDA Tied Triphone \u5716\u4e94\u3001\u591a\u8a9e\u8072\u5b78\u6a21\u578b\u8fa8\u8b58\u53f0\u8a9e\u4e4b\u8fa8\u8b58\u7d50\u679c (4)\u82f1\u8a9e\u5be6\u9a57 \u5716\u516d\u3001\u591a\u8a9e\u8072\u5b78\u6a21\u578b\u8fa8\u8b58\u82f1\u8a9e\u4e4b\u8fa8\u8b58\u7d50\u679c 31.67% 37.21% 41.17% 43.40% 45.21% 48.00% 50.73% 52.81% 53.02% \u516d\u3001\u7d50\u8ad6\u8207\u672a\u4f86\u7814\u7a76\u767c\u5c55\u65b9\u5411 60.00% 51.00% 48.58% 44.56% 50.00% \u7531\u65bc\u76ee\u524d\u7db2\u8def\u8207\u4ea4\u901a\u7684\u767c\u9054\uff0c\u4f7f\u5f97\u5168\u7403\u5316\u6210\u70ba\u5fc5\u7136\u7684\u8da8\u52e2\uff0c\u800c\u591a\u8a9e\u8fa8\u8b58\u5728\u9019\u500b\u793e\u6703 44.23% 38.67% \u4e5f\u6108\u4f86\u6108\u986f\u5f97\u91cd\u8981\u3002\u4f46\u6709\u8a9e\u8a00\u8fa8\u8b58\u7684\u591a\u8a9e\u8fa8\u8b58\u5b58\u5728\u8457\u932f\u8aa4\u758a\u52a0\u7684\u554f\u984c\u3002\u4e0d\u4f7f\u7528\u8a9e\u8a00\u8fa8\u8b58 38.44% 0.00% 10.00% 20.00% 30.00% 40.00% Top1 Top2 Top3 Top4 Top5 \u7684\u591a\u8a9e\u8fa8\u8b58\u65b9\u6cd5\u88ab\u63d0\u51fa\u7528\u4f86\u89e3\u6c7a\u6b64\u554f\u984c\uff0c\u4f46\u4e0d\u4f7f\u7528\u8a9e\u8a00\u8fa8\u8b58\u7684\u65b9\u6cd5\u6709\u4e0d\u540c\u8a9e\u8a00\u9593\u7684\u8072\u5b78 \u6a21\u578b\u9593\u5bb9\u6613\u6df7\u6dc6\u7684\u554f\u984c\u5b58\u5728\uff0c\u800c\u672c\u6587\u63d0\u51fa\u4f7f\u7528\u6f5b\u5728\u72c4\u5f0f\u5206\u4f48\u4f86\u9032\u884c\u8072\u5b78\u6a21\u578b\u5408\u4f75\u4e4b\u9078\u64c7\u3002 Accuracy Triphone Tied Triphone LDA Tied Triphone 0.00% Top1 Top2 Top3 Top4 Top5 Triphone Tied Triphone LDA Tied Triphone \u6240\u6709\u8a9e\u8a00\u97f3\u7d20\u7686\u4ee5 IPA \u8868\u793a\uff0c\u4ee5\u9054\u5230\u53c3\u6578\u5171\u901a\u4e4b\u76ee\u7684\u3002\u4e26\u4e14\u5c07\u97f3\u7d20\u5c0d\u61c9\u5230\u767c\u97f3\u90e8\u4f4d\u548c\u767c \u97f3\u65b9\u6cd5\u4ee5\u6e1b\u5c11\u6578\u91cf\uff0c\u4e26\u4e14\u9054\u5230\u6e1b\u5c11\u904b\u7b97\u91cf\u4e4b\u76ee\u7684\uff0c\u4f7f\u5f97\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u5075\u6e2c\u5668\u5f97\u4ee5\u904b\u7528\u9577 \u8ddd\u96e2\u8a5e\u5f59\u8a9e\u610f\u8cc7\u8a0a\uff0c\u5c07\u5e38\u5e38\u5148\u5f8c\u6210\u5c0d\u51fa\u73fe\u7684\u97f3\u7d20\u9032\u884c\u5408\u4f75\uff0c\u4ee5\u6e1b\u5c11\u8072\u5b78\u6a21\u578b\u7684\u6df7\u6dc6\u3002 \u5be6\u9a57\u7d50\u679c\u986f\u793a\uff0c\u7531\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u6240\u9078\u64c7\u5408\u4f75\u7684\u8072\u5b78\u6a21\u578b\u548c\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u4ee5\u53ca\u672a \u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u76f8\u6bd4\u5728\u55ae\u8a9e\u74b0\u5883\u4e0b\u8fa8\u8b58\u570b\u8a9e\u6700\u9ad8\u6709 10.16%\u7684\u6e96\u78ba\u7387\u6539\u5584\uff0c\u800c\u8fa8\u8b58\u82f1 \u8a9e\u5247\u6709 4.23%\u7684\u6e96\u78ba\u7387\u6539\u5584\u3002\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u6df7\u5408\u8fa8\u8b58\u570b\u53f0\u82f1\u4e09\u8a9e\u6df7\u5408\u7684\u60c5\u6cc1\u4e0b\u6709 0.24% \u7684\u6e96\u78ba\u5ea6\u6539\u5584\uff0c\u8fa8\u8b58\u82f1\u8a9e\u6709 3.16%\u7684\u6e96\u78ba\u5ea6\u6539\u5584\u3002 \u672c\u6587\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u96d6\u7136\u5728\u591a\u6578\u7684\u60c5\u6cc1\u4e0b\u90fd\u53ef\u4ee5\u7372\u5f97\u6e96\u78ba\u7387\u7684\u6539\u5584\uff0c\u4f46\u662f\u6574\u9ad4\u8fa8\u8b58\u7387 \u4ecd\u7136\u504f\u4f4e\uff0c\u591a\u8a9e\u8fa8\u8b58\u76f8\u8f03\u65bc\u55ae\u8a9e\u8fa8\u8b58\u7684\u8072\u5b78\u6a21\u578b\u6578\u91cf\u6703\u96a8\u8457\u8a9e\u8a00\u6578\u91cf\u6210\u500d\u6578\u6210\u9577\uff0c\u800c\u8072 \u5b78\u6a21\u578b\u6578\u91cf\u6108\u591a\u5247\u9700\u8981\u66f4\u591a\u7684\u8a13\u7df4\u8a9e\u6599\u4f86\u8a13\u7df4\u3002\u56e0\u6b64\u591a\u8a9e\u8fa8\u8b58\u7d93\u5e38\u9762\u81e8\u8a9e\u6599\u4e0d\u8db3\u7684\u554f\u984c\uff0c \u96d6\u7136\u7d93\u904e\u6a21\u578b\u5408\u4f75\u5f8c\u9a0e\u8072\u5b78\u6a21\u578b\u6578\u91cf\u4ecd\u7136\u76f8\u7576\u9f90\u5927\u3002\u672c\u6587\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u6700\u7d42\u5408\u4f75\u5f8c\u7684\u8072 126000 \u53e5\uff0c\u6a19\u8a18\u70ba ForPA\uff0c\u5176\u4e2d\u5171\u6709\u97fb\u6bcd 9 \u96b1\u85cf\u99ac\u53ef\u592b\u8072\u5b78\u6a21\u578b\u8a13\u7df4\u4f7f\u7528 39 \u7dad\u7684\u6885\u723e\u5012\u983b\u8b5c\u7cfb\u6578\uff0c\u97f3\u6846(frame)\u5927\u5c0f\u70ba 20ms\uff0c (1)\u53d6\u4ee3\u932f\u8aa4(Substitution Errors)\uff1a\u5c07\u6b63\u78ba\u7684\u97f3\u7d20\u66ff\u63db\u6210\u5176\u4ed6\u97f3\u7d20\u3002 (2)\u522a\u9664\u932f\u8aa4(Deletion Errors)\uff1a\u6c92\u6709\u5c07\u8a72\u8fa8\u8b58\u7684\u97f3\u7d20\u8fa8\u8b58\u51fa\u4f86\u3002 (3)\u63d2\u5165\u932f\u8aa4(Insertion Errors)\uff1a\u672c\u4f86\u6c92\u6709\u7684\u97f3\u7d20\u537b\u984d\u5916\u8fa8\u8b58\u51fa\u4f86\u3002 \u70ba\u4e86\u8a55\u4f30\u672c\u7cfb\u7d71\u4e4b\u6548\u80fd\uff0c\u6211\u5011\u5c07\u5206\u958b\u5206\u6790\u53d6\u4ee3\u932f\u8aa4\u7387\u3001\u522a\u9664\u932f\u8aa4\u7387\u3001\u63d2\u5165\u932f\u8aa4\u7387\u4ee5\u53ca\u5b57 \u932f\u8aa4\u7387(Word Error Rate, WER)\u548c\u6e96\u78ba\u5ea6(Accuracy)\u3002\u8a08\u7b97\u516c\u5f0f\u5982\u4e0b\u5f0f\uff1a Substitution Errors Rate = (\u5f0f 5.1) Deletion Errors Rate = (\u5f0f 5.2) Insertion Errors Rate = \u56e0\u6b64\u53ef\u4ee5\u9a57\u8b49\u672c\u6587\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u4ecd\u7136\u6709\u8f03\u4f73\u7684\u6548\u80fd\u3002 34.88% 38.96% 41.80% 43.43% 44.73% 36.23% 40.33% 43.05% 44.83% 46.34% 37.89% 42.09% 44.89% 60.00% \u5716\u56db\u3001\u591a\u8a9e\u8072\u5b78\u6a21\u578b\u8fa8\u8b58\u570b\u8a9e\u4e4b\u8fa8\u8b58\u7d50\u679c \u5b78\u6a21\u578b\u6578\u91cf\u9060\u5927\u65bc\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\uff0c\u56e0\u6b64\u9700\u8981\u66f4\u591a\u7684\u8a13\u7df4\u8a9e\u6599\u3002\u672a\u4f86\u6211\u5011\u6703\u6301\u7e8c\u7684\u6536 \u672c\u6587\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u4ee5\u8fa8\u8b58\u82f1\u8a9e\u6e96\u78ba\u5ea6\u63d0\u5347\u7387\u9060\u9ad8\u65bc\u570b\u8a9e\u548c\u53f0\u8a9e\uff0c\u5982\u5716 \u516d\u6240\u793a\uff0c\u5728 Top5 \u6642\u6e96\u78ba\u5ea6\u8f03\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u9ad8 3.16%\uff0c\u8207\u672a\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u76f8\u6bd4 \u96c6\u5927\u91cf\u7684\u8a9e\u6599\uff0c\u8b93\u8a13\u7df4\u8cc7\u6599\u66f4\u70ba\u5b8c\u5584\uff0c\u4ee5\u6539\u5584\u8a9e\u6599\u4e0d\u8db3\u7684\u554f\u984c\u3002 (3)\u53f0\u8a9e\u5be6\u9a57 47.86% \u5247\u662f\u9ad8 5.98%\u3002\u5982\u8868 5.9 \u6240\u793a\uff0c\u5728 Top5 \u60c5\u6cc1\u4e0b\uff0c\u53d6\u4ee3\u932f\u8aa4\u76f8\u8f03\u65bc\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u6709 \u76ee\u524d\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u53ea\u4f7f\u7528\u9577\u8ddd\u96e2\u8a5e\u5f59\u8a9e\u610f\u8cc7\u8a0a\u4f86\u9032\u884c\u5408\u4f75\uff0c\u56e0\u6b64\u7576\u53e5\u5b50\u9577\u5ea6\u8f03\u77ed\u7684 46.45% 10.00% 20.00% 30.00% 40.00% 50.00% 3.2%\u7684\u6539\u5584\uff0c\u522a\u9664\u932f\u8aa4\u5247\u662f\u6709 0.63%\u7684\u6539\u5584\uff0c\u4f46\u63d2\u5165\u932f\u8aa4\u5247\u662f\u8f03\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u9ad8 \u6642\u5019\u6e96\u78ba\u5ea6\u6703\u6709\u76f8\u7576\u7a0b\u5ea6\u7684\u4e0b\u964d\uff0c\u56e0\u6b64\u672a\u4f86\u82e5\u53ef\u4ee5\u540c\u6642\u8003\u616e\u77ed\u8ddd\u96e2\u7684\u8cc7\u6599\u7279\u6027\uff0c\u4f8b\u5982\u4e09 \u591a\u8a9e\u8072\u5b78\u6a21\u578b\u8fa8\u8b58\u53f0\u8a9e\u7684\u74b0\u5883\u4e0b\uff0c\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u9078\u64c7\u4e4b\u8072\u5b78\u6a21\u578b\u6e96\u78ba\u5ea6\u5728 Top5 \u6642 0.61%\u3002\u56e0\u6b64\u53ef\u4ee5\u770b\u51fa\u672c\u6587\u63d0\u51fa\u4e4b\u65b9\u6cd5\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u8fa8\u8b58\u82f1\u6587\u53ef\u4ee5\u6e1b\u5c11\u53d6\u4ee3\u932f\u8aa4\u548c\u522a\u9664 \u9023\u97f3\u7d20\u6a21\u578b\u805a\u5408\u6642\u6240\u4f7f\u7528\u7684\u99ac\u5f0f\u8ddd\u96e2\uff0c\u6216\u8a31\u53ef\u4ee5\u540c\u6642\u5728\u77ed\u53e5\u548c\u9577\u53e5\u90fd\u53d6\u5f97\u8f03\u4f73\u7684\u7d50\u679c\u3002 \u7565\u9ad8\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b 0.21%\uff0c\u800c\u9ad8\u672a\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b 7.81%\uff0c\u5982\u5716\u4e94\u6240\u793a\u3002\u5728 Top5 \u932f\u8aa4\uff0c\u800c\u6574\u9ad4\u7684\u6e96\u78ba\u7387\u4e5f\u6709\u6240\u63d0\u5347\u3002 \u6642\u53d6\u4ee3\u932f\u8aa4\u8f03\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u4f4e 0.23%\u3001\u522a\u9664\u932f\u8aa4\u591a 0.02%\u800c\u63d2\u5165\u932f\u8aa4\u5247\u662f\u76f8\u540c\uff0c\u6574 Accuracy \u9ad4\u770b\u4f86\u4ecd\u7565\u512a\u65bc\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u3002\u5728 5.2.2 \u7684\u55ae\u8a9e\u74b0\u5883\u4e0b\u53f0\u8a9e\u8072\u5b78\u6a21\u578b\u5be6\u9a57\u4e4b\u7d50\u679c\u986f \u793a\u6f5b\u85cf\u72c4\u5f0f\u5206\u4f48\u9078\u64c7\u7684\u8072\u5b78\u6a21\u578b\u6e96\u78ba\u7387\u7565\u4f4e\u65bc\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\uff0c\u800c\u5728\u591a\u8a9e\u74b0\u5883\u4e0b\u4e4b\u6e96 \u78ba\u7387\u96d6\u7136\u7565\u6709\u6539\u5584\uff0c\u4f46\u4f9d\u7136\u8207\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u5dee\u8ddd\u4e0d\u5927\u3002\u56e0\u6b64\u6211\u5011\u63a8\u6e2c\u672c\u6587\u6240\u63d0\u51fa\u7684 \u65b9\u6cd5\u53ef\u80fd\u5728\u6211\u5011\u6240\u4f7f\u7528\u7684\u53f0\u8a9e\u8a9e\u6599\u74b0\u5883\u4e0b\u6548\u80fd\u6539\u5584\u6709\u9650\uff0c\u4f46\u5728\u591a\u8a9e\u7684\u60c5\u6cc1\u4e0b\u4ecd\u7136\u53ef\u4ee5\u7565 \u9ad8\u65bc\u805a\u985e\u4e09\u9023\u97f3\u7d20\u6a21\u578b\u3002 31.14% 34.46% 36.19% 37.01% 38.05% 37.15% 39.19% 40.80% 40.15% 43.19% 44.03% 50.00% \u81f4\u8b1d 42.35% 39.79% 36.49% 40.00% \u672c\u7814\u7a76\u627f\u8499\u4e2d\u83ef\u6c11\u570b\u570b\u5bb6\u79d1\u5b78\u59d4\u54e1\u6703\u7d93\u8cbb(99-2221-E-415-006)\u652f\u6301\u65b9\u5f97\u4ee5\u5b8c\u6210\uff0c\u7279 33.59% \u5225\u611f\u8b1d\u3002 (\u5f0f 5.3) Word Error Rate = + + (\u5f0f 5.4) Accuracy = \u2212 (\u5f0f 5.5) \u5716\u4e09\u3001\u4e09\u7a2e\u8072\u5b78\u6a21\u578b\u5728\u570b\u53f0\u82f1\u6df7\u5408\u4e0b\u4e4b\u8fa8\u8b58\u7d50\u679c 0.00% Top1 Top2 Top3 Top4 Top5 Triphone Tied Triphone LDA Tied Triphone 10.00% 20.00% 30.00% Accuracy \u53c3\u8003\u6587\u737b</td></tr></table>",
                "text": "\u7a2e\u3001\u8072\u6bcd 18 \u7a2e\u548c\u9f3b\u97f3\u5c3e\u8072\u6bcd 5 \u7a2e\u3002\u82f1\u6587\u4f7f \u7528\u8a9e\u6599\u70ba\u6210\u5927\u9304\u88fd\u7684\u9ea5\u514b\u98a8\u8a9e\u6599\u5171 808 \u53e5\u3002\u570b\u8a9e\u5be6\u9a57\u8a9e\u6599\u5f9e TCC300 \u4e2d\u9078\u51fa 16kHz, 16bit \u4e4b\u9ea5\u514b\u98a8\u8a9e\u6599\u5171 2676 \u53e5\u3002\u82f1\u6587\u8a9e\u6599\u4f7f\u7528 TIMIT\uff0c\u8a9e\u6599\u70ba 16kHz, 16bit \u652f\u9ea5\u514b\u98a8\u8a9e\u6599\u5171 4620 \u53e5\u3002\u7279\u5fb5\u4f7f\u7528\u6885\u723e\u5012\u983b\u8b5c\u7cfb\u6578(Mel-scale Frequency Cepstral Coefficients, MFCC)\u3001 \u96b1\u85cf\u99ac\u53ef\u592b(HMM)\u8072\u5b78\u6a21\u578b\u8a13\u7df4\u548c\u8072\u5b78\u6a21\u578b\u5408\u4f75\u90e8\u5206\u5247\u662f\u4f7f\u7528\u82f1\u570b\u528d\u6a4b\u5927\u5b78 HTK toolkit \u4f86\u5efa\u7acb\u3002\u6f5b\u85cf\u5f0f\u72c4\u5f0f\u5206\u4f48\u6a21\u578b\u8a13\u7df4\u5247\u662f\u4ee5\u539f\u4f5c\u8005 Blei et al.\u7684 ToolKit \u70ba\u57fa\u790e\u4f86\u9032 \u884c\u4fee\u6539\u3002",
                "type_str": "table"
            }
        }
    }
}