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{
"paper_id": "O15-1002",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:09:59.517020Z"
},
"title": "Exploring Word Embedding and Concept Information for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition",
"authors": [
{
"first": "Ssu-Cheng",
"middle": [],
"last": "\u9673\u601d\u6f84",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Hsiao-Tsung",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {},
"email": "kychen@iis.sinica.edu.tw"
},
{
"first": "Berlin",
"middle": [],
"last": "Hung",
"suffix": "",
"affiliation": {},
"email": "berlin@ntnu.edu.tw"
},
{
"first": "",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Kuan-Yu",
"middle": [],
"last": "\u9673\u51a0\u5b87",
"suffix": "",
"affiliation": {},
"email": ""
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{
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"last": "\u5be6\u9a57\u7d50\u679c\u986f\u793a\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u7684\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u65b9\u6cd5\u76f8\u8f03\u65bc\u7576\u4eca\u6700\u597d\u65b9\u6cd5\u6709\u8f03\u4f73\u7684",
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{
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"last": "\u95dc\u9375\u8a5e\uff1a\u8a9e\u97f3\u8fa8\u8b58\u3001\u8a9e\u8a00\u6a21\u578b\u3001\u8a5e\u5411\u91cf\u8868\u793a\u3001\u6982\u5ff5\u6a21\u578b",
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],
"year": "",
"venue": null,
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"abstract": "Research on deep learning has experienced a surge of interest in recent years. Alongside the rapid development of deep learning related technologies, various",
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"text": "Research on deep learning has experienced a surge of interest in recent years. Alongside the rapid development of deep learning related technologies, various",
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"section": "Abstract",
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{
"text": "distributed representation methods have been proposed to embed the words of a vocabulary as vectors in a lower-dimensional space. Based on the distributed representations, it is anticipated to discover the semantic relationship between any pair of words via some kind of similarity computation of the associated word vectors. With the above background, this article explores a novel use of distributed representations of words for language modeling (LM) in speech recognition. Firstly, word vectors are employed to represent the words in the search history and the upcoming words during the speech recognition process, so as to dynamically adapt the language model on top of such vector representations. Second, we extend the recently proposed concept language model (CLM) by conduct relevant training data selection in the sentence level instead of the document level. By doing so, the concept classes of CLM can be more accurately estimated while simultaneously eliminating redundant or irrelevant information. On the other hand, since the resulting concept classes need to be dynamically selected and linearly combined to form the CLM model during the speech recognition process, we determine the relatedness of each concept class to the test utterance based the word representations derived with either the continue bag-of-words model (CBOW) or the skip-gram model (Skip-gram). Finally, we also combine the above LM methods for better speech recognition performance. Extensive experiments carried out on the MATBN (Mandarin Across Taiwan Broadcast News) corpus demonstrate the utility of our proposed LM methods in relation to several well-practiced baselines. [3, 4] ",
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"text": "\u3002N \u9023(N-gram)\u8a9e\u8a00\u6a21\u578b\u70ba\u8a9e\u97f3\u8fa8\u8b58\u4e4b\u4e2d\u6700\u70ba\u5e38\u898b\u7684\u7d71\u8a08\u5f0f\u8a9e\u8a00\u6a21\u578b\uff0c\u7528\u4f86\u4f30\u6e2c\u6bcf\u4e00 \u500b\u5f85\u9810\u6e2c\u8a5e\u5f59\u5728\u5176\u5148\u524d\u7dca\u9130\u7684 N-1 \u500b\u8a5e\u5f59\u5df2\u77e5\u7684\u60c5\u6cc1\u4e0b\u51fa\u73fe\u7684\u689d\u4ef6\u6a5f\u7387\uff1b\u5047\u8a2d\u6bcf \u4e00\u500b\u8a5e\u5f59\u51fa\u73fe\u7684\u6a5f\u7387\u50c5\u8207\u5b83\u7dca\u9130\u7684\u524d N-1 \u500b\u8a5e\u5f59\u76f8\u95dc\uff0c\u53ef\u4ee5\u900f\u904e\u591a\u9805\u5f0f\u5206\u5e03 (Multinomial Distribution)\u4f86\u8868\u793a\u3002\u7136\u800c N \u9023\u8a9e\u8a00\u6a21\u578b\u50c5\u80fd\u64f7\u53d6\u77ed\u8ddd\u96e2\u7684\u8a5e\u5f59\u898f\u5247 \u8cc7\u8a0a\uff0c\u800c\u7121\u6cd5\u8003\u616e\u9577\u8ddd\u96e2\u7684\u8a9e\u53e5\u6216\u7bc7\u7ae0\u8cc7\u8a0a\uff1b\u7576\u8a5e\u5e8f\u5217\u8d8a\u9577\u6642\u53c3\u6578\u91cf\u8d8a\u591a\uff0c\u4f7f\u5f97 N \u9023\u8a9e\u8a00\u6a21\u578b\u6703\u6709\u7dad\u5ea6\u8a5b\u5492\u7684\u554f\u984c\u3002\u53e6\u4e00\u65b9\u9762\uff0cN \u9023\u8a9e\u8a00\u6a21\u578b\u4ea6\u5bb9\u6613\u9762\u81e8\u8a13\u7df4\u8a9e \u6599\u8207\u6e2c\u8a66\u8a9e\u6599\u4e0d\u5339\u914d(Mismatch)\u800c\u9020\u6210\u4f30\u6e2c\u8aa4\u5dee\u3002\u6709\u9451\u65bc\u6b64\uff0c\u8fd1\u5341\u5e7e\u5e74\u4f86\u8a31\u591a\u52d5 \u614b\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u6280\u8853\u88ab\u63d0\u51fa\uff0c\u7528\u4ee5\u767c\u5c55\u6709\u6548\u7684\u8a9e\u8a00\u6a21\u578b\u8f14\u52a9\u4e26\u5f4c\u88dc\u50b3\u7d71 N \u9023 (N-gram)\u8a9e\u8a00\u6a21\u578b\u4e0d\u8db3\u4e4b\u8655\u3002\u5e38\u898b\u7684\u6709\u5feb\u53d6\u6a21\u578b(Cache Model)[5]\uff0c\u4ee5\u53ca\u5728\u8cc7\u8a0a\u6aa2 \u7d22 \u9818 \u57df \u7684 \u4e3b \u984c \u6a21 \u578b (Topic Model)[6] \u7b49 \u3002 \u5176 \u4e2d \u53c8 \u4ee5 \u6a5f \u7387 \u5f0f \u6f5b \u85cf \u8a9e \u610f \u5206 \u6790 (Probabilistic Latent Semantic",
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{
"text": "EQUATION",
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u505a\u70ba\u8a5e\u7684\u8868\u793a\u6cd5\uff0c\u662f\u900f\u904e\u524d\u994b\u5f0f\u985e\u795e\u7d93\u7db2\u8def(Feed-Forward Neural Network)\u8a13\u7df4\u800c \u6210\u3002\u9019\u7a2e\u5411\u91cf\u8868\u793a\u662f\u5c07\u8a5e\u8868\u793a\u6210\u4e00\u500b\u8f03\u4f4e\u7dad\u5ea6\u7684\u5be6\u6578\u5411\u91cf\u3002\u6bcf\u500b\u8a5e\u5f59\u4e4b\u9593\u7684\u95dc\u4fc2 \u53ef\u4ee5\u5229\u7528\u9918\u5f26\u6216\u662f\u6b50\u5f0f\u8ddd\u96e2\u8a08\u7b97\u627e\u51fa\u5169\u500b\u8a5e\u5411\u91cf\u9593\u7684\u8a9e\u610f\u76f8\u4f3c\u5ea6\uff0c\u6211\u5011\u5c07\u9019\u4e9b\u8a5e \u5411\u91cf\u7a31\u70ba\u8a5e\u8868\u793a\u6cd5(Word Representation or Embedding)\u3002 \u6709\u9451\u65bc\u4f7f\u7528\u50b3\u7d71\u985e\u795e\u7d93\u7db2\u8def\u8a9e\u8a00\u6a21\u578b\u4f86\u8a13\u7df4\u8a5e\u5411\u91cf\u6703\u9020\u6210\u8a13\u7df4\u6642\u9593\u904e\u9577\uff0c Tomas Mikolov \u7b49\u4eba[10] \u65bc\u662f\u63d0\u51fa\u6240 \u8b02 \u7684 \u9023 \u7e8c \u578b \u8a5e \u888b \u6a21 \u578b (Continuous Bag-of-Words Model, CBOW)\u8207\u8df3\u8e8d\u5f0f\u6a21\u578b(Skip-Gram Model, SG)\uff0c\u9019\u5169\u7a2e\u6a21\u578b \u4f7f\u7528\u968e\u5c64\u8edf\u5f0f\u6700\u5927\u5316(Hierarchical Soft-max, HS)[10] \u4ee5 \u53ca \u8ca0 \u4f8b \u63a1 \u6a23 (Negative Sampling, NS) [11]\u65b9\u6cd5\u4f86\u63d0\u9ad8\u8a13\u7df4\u7684\u901f\u5ea6\u4e26\u6539\u5584\u8a13\u7df4\u5f8c\u8a5e\u5411\u91cf\u7684\u8868\u793a\u80fd\u529b\u3002 \u5716\u4e00\u3001\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b\u793a\u610f\u5716 (\u4e00)\u3001\u9023\u7e8c\u578b\u6a21\u578b \u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW)\u8207\u524d\u994b\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u985e\u4f3c\uff0c\u4e0d\u540c\u4e4b\u8655\u5728\u65bc\u9023\u7e8c\u578b\u8a5e\u888b\u6a21 \u578b\u5c07\u975e\u7dda\u6027\u96b1\u85cf\u5c64(Non-Linear Hidden Layer)\u79fb\u9664\uff0c\u4e26\u4e14\u5728\u8f38\u5165\u5c64\u7684\u6240\u6709\u55ae\u8a5e\u7686\u5171 \u4eab\u96b1\u85cf\u5c64\u3002\u5982\u5716\u4e00\u6240\u793a\uff0c\u6b64\u6a21\u578b\u5305\u542b\u4e09\u5c64\uff0c\u5206\u5225\u70ba\u8f38\u5165\u5c64\u3001\u6295\u5f71\u5c64\u3001\u8f38\u51fa\u5c64\u3002\u5df2 \u77e5\u7576\u524d\u8a5e w t \u7684\u4e0a\u4e0b\u6587w t-2 ,w t-1 ,w t+1 ,w t+2 \u7684\u60c5\u6cc1\u4e0b\u9810\u6e2c\u7576\u524d\u8a5ew t \u51fa\u73fe\u7684\u6a5f\u7387\u3002\u5728\u6b64 \u76ee\u6a19\u51fd\u6578\u70ba\u6700\u5927\u5316\u8a13\u7df4\u8a9e\u6599\u5eab\u4e2d\u6240\u6709\u8a5e\u5f59\u5e73\u5747\u7684\u767c\u751f\u6a5f\u7387: 1 T \u2211 log P(w t |w t-k ,\u2026,w t+k ) T-k t=k",
"eq_num": "(1)"
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{
"text": "\u5176\u689d\u4ef6\u6a5f\u7387\u53ef\u4ee5\u900f\u904e Softmax \u51fd\u6578\u8f49\u63db\u70ba: ",
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{
"text": "EQUATION",
"cite_spans": [],
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{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "P(w t |w t-k ,\u2026,w t+k )= e \u2211 e y i i (2) \u5176\u4e2d y ={y 1 ,\u2026, y v }\uff0c\u800c y \u4e2d\u7684\u6bcf\u500b y i \u70ba\u5c0d\u65bc\u6bcf\u4e00\u500b\u8a5e w i \u9084\u672a\u7d93\u904e\u6b63\u898f\u5316\u7684 log \u6a5f \u7387\u503c\uff0c\u8a08\u7b97\u5982\u4e0b\u5f0f: y=b+Uh(w t-k ,\u2026,w t+k ,X) (3) \u5176\u4e2dU\u3001b\u70ba Softmax \u7684\u53c3\u6578\uff0ch \u662f\u5f9e\u77e9\u9663 X \u4e2d\u7684\u8a5e\u5411\u91cf(w t-k \u20d7\u20d7\u20d7\u20d7\u20d7\u20d7 ,\u2026,w t+k \u20d7\u20d7\u20d7\u20d7\u20d7\u20d7\u20d7 )\u52a0\u7e3d\u5e73\u5747\uff0cX\u70ba \u6839\u64da\u6bcf\u500b\u8a5ew i \u7684\u5411\u91cf\u6240\u7d44\u6210\u7684\u77e9\u9663\u3002 (\u4e8c)\u3001\u8df3\u8e8d\u5f0f\u6a21\u578b \u8df3\u8e8d\u5f0f\u6a21\u578b(Skip-gram)\u8207\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW)\u76f8\u53cd\uff0c\u4f7f\u7528\u7576\u524d\u7684\u8a5e\u4f86\u9810\u6e2c\u5468 \u570d\u7684\u8a5e\u3002\u5728\u5df2\u77e5\u7576\u524d\u8a5e w t \u7684\u60c5\u6cc1\u4e0b\uff0c\u9810\u6e2c\u5176\u4e0a\u4e0b\u6587 w t-2 ,w t-1 ,w t+1 ,w t+2 \u7684\u6a5f\u7387\u3002\u7d66 \u5b9a\u4e00\u6bb5\u8a5e\u5e8f\u5217 w 1 ,w 2 ,w 3 ,\u2026,w t \uff0c\u5728\u6b64\u6700\u5927\u5316\u76ee\u6a19\u51fd\u6578: 1 T \u2211 \u2211 log P(w t+k |w t ) -c\u2264k\u2264c,k\u22600 T t=1",
"eq_num": "(4)"
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"sec_num": null
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{
"text": "( | ) = \u2211 ( | ) \u2208 (5) \u5728\u6b64\u52a0\u5165\u53c3\u6578 \u03b1 j \uff0c\u4e26\u4e14\u5047\u8a2d\u53c3\u6578 1 , 2 , \u2026 , \u52a0\u7e3d\u70ba 1\uff0c\u4f7f\u5f97\u8ddd\u96e2\u8a5e \u8d8a\u8fd1\u7684\u8a5e \u7d66\u4e88\u8f03\u5927\u6b0a\u91cd\uff0c\u4ea6\u5373\u5728\u6b77\u53f2\u8a5e\u5e8f\u5217\u4e2d\u8d8a\u9760\u8fd1\u7576\u524d\u8a5e \u7684\u8a5e\u8d8a\u91cd\u8981\u3002 ( | )\u8868 \u793a\u5728\u7d66\u5b9a\u6b77\u53f2\u8a5e\u5e8f\u5217 H i \u4e2d\u8a5e \u4e0b\u9810\u6e2c\u7576\u524d\u8a5e \u7684\u6a5f\u7387\uff0c\u53ef\u4ee5\u7531(6)\u5f0f\u5f97\u5230: (w i |w m )= e w i \u20d7\u20d7\u20d7 \u2022 w m \u20d7\u20d7\u20d7\u20d7\u20d7\u20d7\u20d7 \u2211 e w i \u20d7\u20d7\u20d7 \u2022W \u20d7\u20d7\u20d7 W\u2208V (6) \u5176\u4e2d w i \u20d7\u20d7\u20d7\u20d7 \u70ba\u7576\u524d\u8a5e w i \u7684\u8a5e\u5411\u91cf\u8868\u793a\uff0c w m \u20d7\u20d7\u20d7\u20d7\u20d7\u20d7 \u70ba\u8a5e\u5716\u4e2d\u7684\u5019\u9078\u8a5e w m \u7684\u8a5e\u5411\u91cf\u8868\u793a\uff0c \u800c W \u70ba\u5c0d\u65bc\u8a5e w i \u7684\u6240\u6709\u5019\u9078\u8a5e\u96c6\u5408\uff0c\u6700\u5f8c\u900f\u904e Softmax",
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{
"text": "(8) \uf0e5 \uf0d5 \uf0e5 \uf0d5 \uf0e5 \uf0e5 \uf0ce \uf0a2 \uf03d \uf0a2 \uf0a2 \uf0ce \uf03d \uf0ce \uf0a2 \uf0ce \uf0a2 \uf0a2 \uf03d \uf0a2 \uf0a2 \uf03d C C C C C L l l C L l l i C i C i i i i i i W C P C h P W C P C h P C w P W C P C H P W C P C H w P W H w P 1 1 CLM ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | ( ) | , ( ) , | ( \uf0e5 \uf0d5 \uf0a2 \uf0a2 \uf0a2 \uf0e5 \uf0d5 \uf03d \uf0ce \uf0a2 \uf03d \uf0a2 \uf02d \uf0a2 \uf0a2 \uf0ce \uf03d \uf02d C C C L l l l C L l l l L i i i i i W C P C",
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"content": "<table><tr><td colspan=\"18\">(\u4e8c)\u3001\u57fa\u790e\u5be6\u9a57\u7d50\u679c \u53e6\u5916\u5728\u8a08\u7b97\u6e2c\u8a66\u8a9e\u53e5\u8207\u6982\u5ff5\u7fa4\u805a\u76f8\u4f3c\u5ea6\u90e8\u5206\uff0c\u6211\u5011\u4f7f\u7528\u8a5e\u5411\u91cf\u8868\u793a\u4e26\u900f\u904e\u9918\u5f26\u65b9 \u91cf\u904e\u5c11\u800c\u7121\u6cd5\u63cf\u7e6a\u51fa\u5176\u6982\u5ff5\u7684\u7279\u6027\uff0c\u56e0\u6b64\u7fa4\u805a\u7684\u6578\u76ee\u4ea6\u662f\u6703\u5f71\u97ff\u8fa8\u8b58\u7d50\u679c\u7684\u91cd\u8981 \u8ca2\u737b\u53ef\u4ee5\u5206\u70ba\u5169\u500b\u90e8\u5206: \u7b2c\u4e00\u90e8\u5206\uff0c\u672c\u8ad6\u6587\u5c07\u8a5e\u5411\u91cf\u8868\u793a\u8cc7\u8a0a\u61c9\u7528\u65bc\u8a5e\u5716\u641c\u5c0b\u4e4b</td></tr><tr><td colspan=\"18\">W \u672c\u8ad6\u6587\u5c07\u8a5e\u5411\u91cf\u8868\u793a\u6cd5\u878d\u5165\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u4e2d\uff0c\u4e26\u4ee5\u5f0f(8)\u6240\u793a\u7684\u8a5e\u96d9\u9023\u6982\u5ff5\u8a9e\u8a00 C P C h h P C h P C h w P W H w P 2 1 1 BCLM ) | ( ) , | ( ) | ( ) , | ( ) , | ( \u5716\u56db\u3001\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u6d41\u7a0b\u5716 (\u4e00)\u3001\u7d50\u5408\u8a5e\u5411\u91cf\u8868\u793a\u8207\u6982\u5ff5\u8cc7\u8a0a\u65bc\u8a9e\u8a00\u6a21\u578b \u6a21\u578b(BCLM)\u70ba\u4f8b\u3002\u9996\u5148\uff0c\u5728\u8abf\u9069\u8a9e\u6599\u6587\u4ef6\u96c6\u5167\u4e4b\u6587\u4ef6\u7531\u4e00\u7d44\u6982\u5ff5\u985e\u5225 C \u4f86\u8868\u793a\uff0c \u4ee5\u7fa4\u805a\u4e4b\u9593\u7684\u76f8\u4f3c\u5ea6\u8fd1\u4f3c\u8a9e\u53e5\u6982\u5ff5\u8868\u9054\u7684\u6db5\u610f\u3002\u5728\u8abf\u9069\u8a9e\u6599\u4e2d\u4ee5\u53e5\u5b50\u7684\u5c64\u6b21\u505a\u6a21 \u578b\u8a13\u7df4\u8cc7\u6599\u9078\u53d6\u4e4b\u4f9d\u64da\uff0c\u5c07\u5177\u6709\u76f8\u4f3c\u8a9e\u610f\u6216\u662f\u76f8\u540c\u6982\u5ff5\u7684\u8a9e\u53e5\u6b78\u70ba\u540c\u4e00\u500b\u985e\u5225 \u4e2d\uff0c\u4f7f\u5f97\u7d93\u7531\u8abf\u9069\u8a9e\u6599\u4e2d\u8a13\u7df4\u51fa\u7684\u6982\u5ff5\u985e\u5225\u66f4\u70ba\u5177\u4ee3\u8868\u6027\u3002\u5176\u4e2d W \u4ee3\u8868\u8a9e\u8005\u6240 \u8b1b\u8a9e\u53e5\u6b32\u8868\u9054\u7684\u8a9e\u8a00\u8cc7\u8a0a\uff0c\u5728\u6b64\u4ee5\u8a9e\u97f3\u8fa8\u8b58\u521d\u6b65\u6240\u7522\u751f\u7684\u8a5e\u5716(Word Graph)\u4f86\u8fd1 \u4f3c\u3002 \u800c P(C|W)\u662f\u900f\u904e\u8a9e\u8a00\u8cc7\u8a0a W \u8207\u6bcf\u4e00\u500b\u6982\u5ff5\u985e\u5225 C \uff0c\u4ee5\u8a5e\u5411\u91cf\u8868\u793a(Word Embedding)\u7684\u65b9\u5f0f\uff0c\u5148\u5c07\u8a5e\u8f49\u63db\u6210\u5411\u91cf\u7684\u5f62\u5f0f\uff0c\u63a5\u8457\u8a08\u7b97\u5176\u9918\u5f26\u76f8\u4f3c\u5ea6\u800c\u5f97\u3002\u5176 \u4e2d\u8a5e\u5411\u91cf\u8868\u793a\u662f\u7531\u9023\u7e8c\u578b\u6a21\u578b(Continue Bag-of-Words Model)\u6216\u662f\u8df3\u8e8d\u5f0f\u6a21\u578b (Skip-gram Model)\u751f\u6210\u3002 ( | )\u8868\u793a\u6982\u5ff5\u985e\u5225 C \u9810\u6e2c\u8a5e\u5f59 \u7684\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u6a5f \u7387\uff0c\u53ef\u4ee5\u900f\u904e\u6700\u5927\u5316\u76f8\u4f3c\u6a5f\u7387\u4f30\u6e2c\u800c\u5f97\u3002 \u56db\u3001 \u5be6\u9a57\u8a2d\u5b9a\u8207\u7d50\u679c\u8a0e\u8ad6 (\u4e00)\u3001\u5be6\u9a57\u8a9e\u6599 \u672c\u7814\u7a76\u6240\u9032\u884c\u4e4b\u8a9e\u97f3\u8fa8\u8b58\u5be6\u9a57\u662f\u4f7f\u7528\u53f0\u5e2b\u5927\u6240\u81ea\u884c\u7814\u767c\u7684\u5927\u8a5e\u5f59\u9023\u7e8c\u8a9e\u97f3\u8fa8\u8b58 \u7cfb\u7d71(\u8a5e\u5178\u5927\u5c0f\u7d04\u70ba 7 \u842c 2 \u5343\u8a5e)[14]\u4ee5\u53ca\u516c\u8996\u96fb\u8996\u65b0\u805e\u8a9e\u97f3\u8a9e\uf9be\u5eab(Mandarin Across Taiwan Broadcast News, MATBN)[15]\u3002\u6b64\u65b0\u805e\u8a9e\u97f3\u8a9e\uf9be\u5eab\u662f\u7531\u4e2d\u592e\u7814\u7a76\u9662 \u8cc7\u8a0a\u6240\u53e3\u8a9e\u5c0f\u7d44\u8017\u6642\u4e09\uf98e(2001~2003)\u8207\u516c\u5171\u96fb\u8996\u53f0[PTS]\u5408\u4f5c\uf93f\u88fd\u5b8c\u6210\u3002\u6211\u5011\u521d Model)\uff0c\u6b64\u8a9e\u8a00\u6a21\u578b\u662f\u4f7f\u7528 SRI Language Modeling Toolkit (SRILM)[17]\u8a13\u7df4\u800c \u5f97\uff0c\u63a1\u7528 Good-Turning \u5e73\u6ed1\u5316\u65b9\u6cd5\u4f86\u89e3\u6c7a\u8cc7\u6599\u7a00\u758f\u7684\u554f\u984c\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u6211\u5011\u4ea6 \u8490\u96c6\u540c\u70ba\u516c\u8996\u96fb\u8996\u65b0\u805e\u8a9e\u6599\u5eab\u4e2d\u7684\u540c\u9818\u57df\u6587\u4ef6\u505a\u70ba\u8abf\u9069\u8a9e\u6599\u5eab\uff0c\u7528\u4f86\u4f30\u6e2c\u672c\u8ad6\u6587 \u6240\u63a2\u8a0e\u7684\u5404\u5f0f\u505a\u70ba\u8abf\u9069\u4e4b\u7528\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u7e3d\u5171\u7d04\u4e09\u5343\u516d\u767e\u56db\u4e09\u53e5\u8a9e\u53e5\u3002\u672c\u8ad6\u6587\u5be6 \u9a57\u6240\u4f7f\u7528\u4e4b\u8a9e\u97f3\u8a9e\u6599\u5eab\u4ee5\u53ca\u6587\u5b57\u8a9e\u6599\u5eab\u7684\u627c\u8981\u7d71\u8a08\u8cc7\u8a0a\u5206\u5225\u5982\u8868\u4e00\u8207\u8868\u4e8c\u6240\u793a\u3002 \u8868\u4e00 \u8a9e\u97f3\u8fa8\u8b58\u5be6\u9a57\u4f7f\u7528\u4e4b\u8a9e\u97f3\u8a9e\u6599\u7d71\u8a08\u8cc7\u8a0a \u8a5e\u5178\u5927\u5c0f \u53e5\u6578 \u9577\u5ea6(\u5c0f\u6642) \u8aaa\u8a71\u901f\u5ea6 \u8a9e\u6599 \u7d04 72000 \u8a5e 292 \u7d04 1.5 8.52 \u5b57/\u79d2 \u8868\u4e8c \u8a9e\u8a00\u6a21\u578b\u4f30\u6e2c\u6240\u4f7f\u7528\u80cc\u666f\u6587\u5b57\u8a9e\u6599\u4ee5\u53ca\u8abf\u9069\u6587\u5b57\u8a9e\u6599\u7d71\u8a08\u8cc7\u8a0a \u80cc\u666f\u8a9e\u6599 \u7d04 80,000,000 2,068,991 \u5728\u57fa\u790e\u5be6\u9a57\u90e8\u5206\uff0c\u9996\u5148\u50c5\u4f7f\u7528\u80cc\u666f\u8a9e\u8a00\u6a21\u578b\u65bc\u4e2d\u6587\u5927\u8a5e\u5f59\u9023\u7e8c\u8a9e\u97f3\u8fa8\u8b58\uff0c\u89c0\u5bdf\u5176 \u5b57\u8fa8\u8b58\u932f\u8aa4\u7387(Character Error Rate, CER)\uff0c\u6211\u5011\u4ea6\u6bd4\u8f03\u540c\u9818\u57df\u8a9e\u6599\u8a13\u7df4\u7684\u8a9e\u8a00\u6a21 \u578b\u7d50\u5408\u80cc\u666f\u8a9e\u8a00\u6a21\u578b\u7684\u5b57\u932f\u8aa4\u7387\u3002\u53e6\u5916\uff0c\u6211\u5011\u4ee5\u8a5e\u5716\u6700\u4f73\u89e3\u78bc(Oracle)\u4f5c\u70ba\u8a9e\u97f3 \u8fa8\u8b58\u6548\u80fd\u7684\u4e0a\u754c\uff1b\u8a5e\u5716\u4e2d\u6700\u4f73\u89e3\u78bc\u662f\u5229\u7528\u52d5\u614b\u898f\u5283\u65b9\u5f0f\uff0c\u627e\u51fa\u8a5e\u5716\u4e2d\u5b57\u932f\u8aa4\u7387\u6700 \u4f4e\u4e4b\u8def\u5f91\u3002\u57fa\u790e\u5be6\u9a57\u65bc\u6e2c\u8a66\u96c6\u4e4b\u5b57\u8fa8\u8b58\u7387\u7d50\u679c\u5982\u8868\u4e09\u6240\u793a\u3002 \u8868\u4e09\u3001\u8a9e\u97f3\u8fa8\u8b58\u57fa\u790e\u5be6\u9a57\u4e4b\u5b57\u8fa8\u8b58\u7387(%)\u7d50\u679c \u5b57\u932f\u8aa4\u7387(%) \u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(UBG) 34.30 \u80cc\u666f\u96d9\u9023\u8a9e\u8a00\u6a21\u578b(BBG) 22.24 \u80cc\u666f\u4e09\u9023\u8a9e\u8a00\u6a21\u578b(TBG) 20.22 \u540c\u9818\u57df\u96d9\u9023\u8a9e\u8a00\u6a21\u578b+TBG 19.12 \u540c\u9818\u57df\u4e09\u9023\u8a9e\u8a00\u6a21\u578b+TBG 19.04 \u8a5e\u5716\u4e2d\u6700\u4f73\u89e3\u78bc(Oracle) 7.72 \u672c\u8ad6\u6587\u5e0c\u671b\u5229\u7528\u8a5e\u5411\u91cf\u8868\u793a\u627e\u5230\u8a5e\u5f59\u9593\u5f7c\u6b64\u7684\u8a9e\u610f\u95dc\u4fc2\uff0c\u5229\u7528\u8a5e\u5411\u91cf\u8868\u793a\u65bc\u8a9e\u97f3 \u8fa8\u8b58\u7684\u8a5e\u5716\u641c\u5c0b\u4e2d\uff0c\u5e0c\u671b\u85c9\u6b64\u80fd\u9054\u5230\u63d0\u5347\u8fa8\u8b58\u7387\u7684\u6548\u679c\u3002\u8868\u56db\u70ba\u6bd4\u8f03\u4e0d\u540c\u7dad\u5ea6\u4ee5 \u53ca\u4e0d\u540c\u8a5e\u5411\u91cf\u8868\u793a(Skip-gram, CBOW)\u65bc\u8a5e\u5716\u641c\u5c0b\u7684\u5b57\u932f\u8aa4\u7387\u7d50\u679c\uff0c\u5728\u6b64\u7dad\u5ea6\u8a2d \u5b9a\u4ee5 10 \u81f3 50 \u4f5c\u70ba\u5be6\u9a57\u4e4b\u6bd4\u8f03\uff0c\u4ee5\u8f03\u5c0f\u7dad\u5ea6\u4e4b\u5dee\u7570\u6bd4\u8f03\uff0c\u6e1b\u5c11\u5176\u8a08\u7b97\u8907\u96dc\u5ea6\u3002 \u8868\u56db\u3001\u61c9\u7528\u8a5e\u5411\u91cf\u8868\u793a\u65bc\u8a5e\u5716\u641c\u5c0b\u4e2d\u4e4b\u5b57\u932f\u8aa4\u7387(%)\u6bd4\u8f03\u8868 \u7dad\u5ea6\u5927\u5c0f \u8df3\u8e8d\u5f0f\u6a21\u578b(Skip-gram) \u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW) 10 19.85 19.86 20 19.85 19.87 30 19.83 19.84 40 19.85 19.86 50 19.85 19.84 \u7531\u8868\u56db\u4e2d\u53ef\u4ee5\u770b\u51fa\u878d\u5165\u8a5e\u5411\u91cf\u8868\u793a\u7684\u8cc7\u8a0a\u65bc\u8a5e\u5716\u641c\u5c0b\u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u5f88\u660e\u986f\u5730\u89c0\u5bdf \u51fa\uff0c\u52a0\u5165\u8a5e\u5411\u91cf\u7684\u8cc7\u8a0a\u5c0d\u65bc\u8a9e\u97f3\u8fa8\u8b58\u6e96\u78ba\u7387\u7684\u63d0\u5347\u6709\u5e6b\u52a9\u3002\u4e0d\u8ad6\u662f\u7528\u8df3\u8e8d\u5f0f\u6a21\u578b (Skip-gram)\u9084\u662f\u4f7f\u7528\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW)\u6240\u8a13\u7df4\u5f97\u5230\u7684\u8a5e\u5411\u91cf\u8868\u793a\uff0c\u5c07\u5176\u61c9 \u7528\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u8a5e\u5716\u641c\u5c0b\u4e4b\u4e2d\uff0c\u5b57\u932f\u8aa4\u7387\u5f9e\u539f\u672c\u53ea\u4f7f\u7528\u8a5e\u5716\u641c\u5c0b\u6642\u4e4b\u5b57\u932f\u8aa4\u7387 20.2 \u4e0b\u964d\u81f3 19.83 (\u4f7f\u7528 Skip-gram)\uff0c\u7372\u5f97\u4e0d\u932f\u7684\u6548\u80fd\u63d0\u5347\u3002 (\u56db) \u7d50\u5408\u8a5e\u5411\u91cf\u8868\u793a\u8cc7\u8a0a\u65bc\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u4e4b\u5be6\u9a57\u7d50\u679c \u672c\u8ad6\u6587\u5617\u8a66\u5c07\u8a5e\u5411\u91cf\u8cc7\u8a0a\u61c9\u7528\u65bc\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u4e4b\u4e2d\uff0c\u5728\u672c\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u5c07\u8abf\u9069\u8a9e \u6599\u4ee5\u53e5\u5b50\u70ba\u55ae\u4f4d\uff0c\u5229\u7528 K-means \u5206\u7fa4\u6cd5\u5c07\u8abf\u9069\u8a9e\u6599\u4e2d\u7684\u8a9e\u53e5\u5206\u70ba\u591a\u500b\u6982\u5ff5\u985e\u5225\u3002 \u95dc\u9375\u3002 \u4e2d\uff0c\u5728\u8a9e\u97f3\u8fa8\u8b58\u7684\u904e\u7a0b\u4e2d\uff0c\u5c0d\u65bc\u52d5\u614b\u7522\u751f\u4e4b\u6b77\u53f2\u8a5e\u5e8f\u5217\u8207\u5019\u9078\u8a5e\u6539\u4ee5\u8a5e\u5411\u91cf\u8868\u793a \u5f0f\u8a08\u7b97\u5176\u76f8\u4f3c\u5ea6\u3002\u672c\u5be6\u9a57\u6bd4\u8f03\u50b3\u7d71\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b(BCLM)\u8207\u7d50\u5408\u8a5e\u5411\u91cf\u8868\u793a\u65bc\u6982 \u5ff5\u8a9e\u8a00\u6a21\u578b(\u7c21\u7a31\u70ba BCLM:WE)\u7686\u4f5c\u7528\u65bc\u4e0d\u540c\u7fa4\u805a\u6578\u76ee\u4e4b\u5b57\u932f\u8aa4\u7387\u7d50\u679c;\u4e0a\u8ff0\u5169 \u7684\u65b9\u5f0f\u4f86\u5efa\u7acb\u5176\u5c0d\u61c9\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u900f\u904e\u6b64\u7a2e\u8868\u793a\u65b9\u5f0f\u800c\u80fd\u7372\u53d6\u5230\u66f4\u591a\u8a5e\u5f59\u9593\u7684\u8a9e (\u4e94) \u5404\u5f0f\u8a9e\u8a00\u6a21\u578b\u4e4b\u5be6\u9a57\u7d50\u679c\u6bd4\u8f03 \u610f\u8cc7\u8a0a\uff0c\u4ee5\u63d0\u5347\u8fa8\u8b58\u7684\u6e96\u78ba\u5ea6\u3002\u7b2c\u4e8c\u90e8\u5206\uff0c\u6211\u5011\u91dd\u5c0d\u65b0\u8fd1\u88ab\u63d0\u51fa\u7684\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b \u7a2e\u65b9\u6cd5\u7686\u8207\u80cc\u666f\u4e09\u9023\u8a9e\u8a00\u6a21\u578b\u505a\u7dda\u6027\u7d50\u5408\u3002\u672c\u5be6\u9a57\u63a1\u7528\u8df3\u8e8d\u5f0f\u6a21\u578b(Skip-gram) \u4f5c\u70ba\u8a5e\u5411\u91cf\u8a13\u7df4\uff0c\u76f8\u8f03\u65bc\u9023\u7e8c\u578b\u6a21\u578b(CBOW) \u6709\u8f03\u4f73\u5be6\u9a57\u7d50\u679c\u3002 \u5176\u4e2d BCLM:WE(10)\u8868\u793a\u4f7f\u7528\u8df3\u8e8d\u5f0f\u6a21\u578b\u8a13\u7df4\u7dad\u5ea6\u70ba 10 \u4e4b\u8a5e\u5411\u91cf\uff0c\u7d50\u5408\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b \u7684\u5be6\u9a57\u7d50\u679c\u3002\u5be6\u9a57\u7d50\u679c\u5982\u8868\u4e94\u6240\u793a\uff0c\u5716\u4e94\u4ee5\u6298\u7dda\u5716\u65b9\u5f0f\u5448\u73fe\u5176\u5be6\u9a57\u7d50\u679c\u3002 \u8868\u4e94\u3001\u7d50\u5408\u8a5e\u5411\u91cf\u8cc7\u8a0a\u65bc\u6982\u5ff5\u6a21\u578b\u4e4b\u4e0d\u540c\u7fa4\u805a\u6578\u7684\u5b57\u932f\u8aa4\u7387(%)\u6bd4\u8f03\u8868 \u7fa4\u805a\u500b\u6578 2 4 8 16 32 BCLM 19.31 19.14 19.58 19.54 19.59 BCLM:WE(10) 18.89 19.05 19.40 19.39 19.52 BCLM:WE(20) 18.90 19.05 19.40 19.39 19.52 BCLM:WE(30) 18.89 19.04 19.40 19.39 19.52 BCLM:WE(40) 18.88 19.05 19.40 19.39 19.52 BCLM:WE(50) 18.88 19.04 19.40 19.39 19.52 \u5716\u4e94\u3001\u7d50\u5408\u8a5e\u5411\u91cf\u8cc7\u8a0a\u65bc\u6982\u5ff5\u6a21\u578b\u4e4b\u4e0d\u540c\u7fa4\u805a\u6578\u7684\u5b57\u932f\u8aa4\u7387(%)\u6bd4\u8f03\u5716 \u7531\u5716\u4e94\u6211\u5011\u53ef\u4ee5\u770b\u51fa\u7d50\u5408\u8a5e\u5411\u91cf\u8868\u793a\u65bc\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b(BCLM:WE)\u4e2d\u4e4b\u5b57\u932f \u8aa4\u7387\u76f8\u8f03\u65bc\u50b3\u7d71\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b(BCLM)\u7686\u6709\u8f03\u597d\u7684\u8868\u73fe\uff0c\u7576\u7fa4\u805a\u6578\u76ee\u70ba 2 \u6642\uff0c\u4f7f \u7528\u8df3\u8e8d\u5f0f\u6a21\u578b(Skip-gram) \u8a13 \u7df4 \u5f97 \u5230 \u7684 \u8a5e \u5411 \u91cf \u8868 \u793a \u65bc \u6982 \u5ff5 \u8a9e \u8a00 \u6a21 \u578b (BCLM:WE(40))\u7576\u7dad\u5ea6\u70ba 40 \u6642\uff0c\u5b57\u932f\u8aa4\u7387\u53ef\u964d\u4f4e\u81f3 18.88\u3002\u53e6\u5916\uff0c\u4ea6\u53ef\u7531\u5716\u4e94\u4e2d \u8ad6\u6587\u4ee5\u6b64\u70ba\u767c\u60f3\uff0c\u63d0\u51fa\u5c07\u5206\u6563\u5f0f\u8868\u793a\u6cd5\u61c9\u7528\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u8a9e\u8a00\u6a21\u578b\u4e2d\u4f7f\u7528\u3002\u4e3b\u8981 \u770b\u51fa\u7576\u7fa4\u805a\u6578\u76ee\u589e\u52a0\u6642\u6709\u5229\u65bc\u6a21\u578b\u7684\u63cf\u8ff0\uff0c\u4f46\u662f\u7531\u65bc\u5206\u7fa4\u6578\u904e\u591a\u6703\u5c0e\u81f4\u6bcf\u7fa4\u8cc7\u6599 \u5716\u516d\u70ba\u5404\u5f0f\u8a9e\u8a00\u6a21\u578b\u8207\u80cc\u666f\u4e09\u9023\u8a9e\u8a00\u6a21\u578b(TBG)\u7d50\u5408\u5f8c\u4e4b\u5b57\u932f\u8aa4\u7387\u7d50\u679c\u6bd4\u8f03\uff0c\u5176 \u4e2d Baseline \u70ba\u8a5e\u5716\u641c\u5c0b(Word Graph Rescoring)\u50c5\u4f7f\u7528\u80cc\u666f\u4e09\u9023\u6a21\u578b\u7d50\u679c\uff0c\u5176\u5b57\u932f \u8aa4\u7387\u70ba 20.22;\u800c CBOW \u8207 Skip-gram \u70ba\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u5c07\u8a5e\u5411\u91cf\u8868\u793a\u61c9\u7528\u65bc\u8a5e\u5716\u641c \u5c0b\u4e4b\u5be6\u9a57\u7d50\u679c\uff0c\u76f8\u8f03\u65bc\u6c92\u6709\u4f7f\u7528\u8a5e\u5411\u91cf\u8868\u793a\u65bc\u8a5e\u5716\u641c\u5c0b\u7d50\u679c\u6709 0.39 \u7d55\u5c0d\u5b57\u932f\u8aa4 \u7387\u4e0b\u964d\u3002\u63a5\u8457\uff0c\u6211\u5011\u6bd4\u8f03\u6f5b\u85cf\u8a9e\u610f\u5206\u6790 (Latent Semantic Analysis, LSA)[18]\u3001\u6a5f \u7387\u5f0f\u6f5b\u85cf\u8a9e\u610f\u5206\u6790(Probabilistic Latent Semantic Analysis, PLSA)[7]\u3001\u72c4\u5229\u514b\u91cc\u5206 \u914d(Latent Dirichlet Allocation, LDA)[8]\u3001\u95dc\u806f\u6a21\u578b(Relevance Model, RM)[19, 20]\u3001 \u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u8a9e\u8a00\u6a21\u578b(Recurrent Neural Network, RNN)[21]\u3001\u6982\u5ff5\u6a21\u578b (Bigram Concept Language Model, BCLM)[12]\u4ee5\u53ca\u672c\u8ad6\u6587\u63d0\u51fa\u7d50\u5408\u8a5e\u5411\u91cf\u8868\u793a\u65bc \u6982\u5ff5\u8a9e\u8a00\u6a21\u578b(BCLM:WE) \u4e4b \u5be6 \u9a57 \u7d50 \u679c \u3002 \u6700\u5f8c\uff0cBCLM:WE+CBOW \u8207 BCLM:WE+Skip-gram \u70ba\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u7684\u5169\u7a2e\u65b9\u6cd5\u7d50\u5408(\u4ea6\u5373\u7b2c\u4e8c\u7bc0\u4ee5\u53ca\u7b2c\u4e09\u7bc0 \u6240\u63d0\u51fa\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u65b9\u6cd5\u4e4b\u7d50\u5408)\uff0c\u5be6\u9a57\u679c\u986f\u793a\uff0c\u5169\u8005\u7d50\u5408\u904e\u5f8c\u6548\u679c\u70ba\u6700\u597d\uff0c\u5b57 \u932f\u8aa4\u7387\u53ef\u4e0b\u964d\u81f3 18.70\u3002\u7531\u5716\u516d\u7d50\u679c\u89c0\u5bdf\u5f97\u77e5\uff0c\u672c\u8ad6\u6587\u63d0\u51fa\u5c07\u8a5e\u5411\u91cf\u8868\u793a\u61c9\u7528\u65bc \u8a9e\u8a00\u6a21\u578b\u4e2d\uff0c\u5c0d\u8a9e\u97f3\u8fa8\u8b58\u7684\u63d0\u5347\u78ba\u5be6\u6709\u5e6b\u52a9\u3002 \u5716\u516d\u3001\u5404\u5f0f\u8a9e\u8a00\u6a21\u578b\u4e4b\u5b57\u932f\u8aa4\u7387(%)\u7d50\u679c\u6bd4\u8f03\u5716 \u4e94\u3001 \u7d50\u8ad6\u8207\u672a\u4f86\u5c55\u671b \u8fd1\u5e74\u4f86\u6df1\u5ea6\u5b78\u7fd2(Deep Learning)\u6fc0\u8d77\u4e00\u80a1\u7814\u7a76\u71b1\u6f6e\uff1b\u96a8\u8457\u6df1\u5ea6\u5b78\u7fd2\u7684\u767c\u5c55\u800c\u6709\u5206 \u6563\u5f0f\u8868\u793a\u6cd5(Distributed Representation)\u7684\u7522\u751f\u3002\u6b64\u7a2e\u8868\u793a\u65b9\u5f0f\uff0c\u4e0d\u50c5\u80fd\u4ee5\u8f03\u4f4e\u7dad \u5ea6\u7684\u5411\u91cf\u8868\u793a\u8a5e\u5f59\uff0c\u9084\u80fd\u85c9\u7531\u5411\u91cf\u9593\u7684\u904b\u7b97\uff0c\u627e\u51fa\u4efb\u5169\u8a5e\u5f59\u4e4b\u9593\u7684\u8a9e\u610f\u95dc\u4fc2\u3002\u672c (Concept Language Model)\u52a0\u4ee5\u6539\u9032\uff0c\u5728\u8abf\u9069\u8a9e\u6599\u4e2d\u4ee5\u53e5\u5b50\u7684\u5c64\u6b21\u505a\u6a21\u578b\u8a13\u7df4\u8cc7\u6599 \u9078\u53d6\u4e4b\u4f9d\u64da\uff0c\u53bb\u6389\u591a\u9918\u4e14\u4e0d\u76f8\u95dc\u7684\u8cc7\u8a0a\uff0c\u4f7f\u5f97\u7d93\u7531\u8abf\u9069\u8a9e\u6599\u4e2d\u8a13\u7df4\u51fa\u7684\u6982\u5ff5\u985e\u5225 \u66f4\u70ba\u5177\u4ee3\u8868\u6027\uff0c\u800c\u80fd\u5e6b\u52a9\u52d5\u614b\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5728\u8a9e\u97f3\u8fa8\u8b58\u904e\u7a0b\u4e2d\uff0c\u6703 \u9078\u64c7\u76f8\u95dc\u7684\u6982\u5ff5\u985e\u5225\u4f86\u52d5\u614b\u7d44\u6210\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\uff0c\u800c\u6b64\u662f\u900f\u904e\u8a5e\u5411\u91cf\u8868\u793a\u7684\u65b9\u5f0f\u4f86 \u4f30\u7b97\uff0c\u85c9\u7531\u8a5e\u5411\u91cf\u8868\u793a\u8a18\u9304\u6bcf\u4e00\u500b\u6982\u5ff5\u985e\u5225\u5167\u8a5e\u5f59\u5f7c\u6b64\u9593\u7684\u8a9e\u610f\u95dc\u4fc2\u3002\u6700\u5f8c\uff0c\u6211 \u5011\u5617\u8a66\u5c07\u4e0a\u8ff0\u5169\u7a2e\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u6280\u8853\u505a\u7d50\u5408\u3002\u6839\u64da\u5be6\u9a57\u7d50\u679c\u986f\u793a\uff0c\u672c\u8ad6\u6587\u63d0\u51fa\u5c07 \u8a5e\u5411\u91cf\u8868\u793a(Word Representation)\u61c9\u7528\u65bc\u8a9e\u8a00\u6a21\u578b\u4e2d\uff0c\u5c0d\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u6e96\u78ba\u7387\u63d0 \u5347\u78ba\u5be6\u6709\u5e6b\u52a9\u3002 \u672a\u4f86\uff0c\u6211\u5011\u5e0c\u671b\u5c07\u8a5e\u5411\u91cf\u8868\u793a\u7684\u8cc7\u8a0a\u61c9\u7528\u65bc\u5176\u4ed6\u7684\u8a9e\u8a00\u6a21\u578b\u4e4b\u4e2d\uff0c\u4f8b\u5982\u61c9\u7528 \u65bc\u95dc\u806f\u6a21\u578b\u3001\u8a5e\u6982\u5ff5\u8a9e\u8a00\u6a21\u578b\u7b49\u3002\u6b64\u5916\uff0c\u6211\u5011\u5e0c\u671b\u4f9d\u64da\u8a5e\u5716\u641c\u5c0b\u7684\u7d50\u679c\u7d50\u5408\u5176\u4ed6 \u8a9e\u8a00\u6a21\u578b\u5f8c\uff0c\u5728\u7b2c\u4e8c\u968e\u6bb5\u7684 N \u689d\u6700\u4f73\u7d50\u679c(N-Best)\u91cd\u65b0\u6392\u540d\u6642\uff0c\u4f7f\u7528\u9577\u77ed\u671f\u8a18\u61b6 \u985e\u795e\u7d93\u7db2\u8def\u6a21\u578b\u3001\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u7b49\u8a9e\u8a00\u6a21\u578b\u91cd\u65b0\u6392\u5e8f\uff0c\u5e0c\u671b\u85c9\u7531\u6b64\u65b9\u6cd5\u9054\u5230 \u8fa8\u8b58\u6548\u80fd\u7684\u63d0\u5347\u3002 \u6b65\u9078\u64c7\u5916\u5834\u63a1\u8a2a\u8a18\u8005\u8a9e\u6599\u4f5c\u70ba\u5be6\u9a57\u984c\u6750\uff0c\u5c07\u5176\u4e2d\u7d04 25 \u6709\u516b\u5343\u842c\u500b\u8a5e) \u505a \u70ba \u80cc \u666f \u8a9e \u6599 \u5eab \u7528 \u4f86 \u8a13 \u7df4 \u4e09 \u9023 \u8a9e \u8a00 \u6a21 \u578b (Trigram Language \u8a9e\u6599 \u8a5e\u6578 \u53e5\u6578 \u8abf\u9069\u8a9e\u6599 \u7d04 1,000,000 3,643 (\u4e09) \u5c07\u8a5e\u5411\u91cf\u8868\u793a\u61c9\u7528\u65bc\u8a5e\u5716\u641c\u5c0b\u4e4b\u5be6\u9a57\u7d50\u679c \u53c3\u8003\u6587\u737b</td></tr><tr><td>P</td><td>(</td><td>h 1</td><td>|</td><td>C</td><td>)</td><td>2</td><td>P</td><td>(</td><td>h</td><td>|</td><td>h</td><td>1</td><td>,</td><td>)</td><td>(</td><td>|</td><td>)</td></tr></table>",
"text": "\u5c0f\u6642\u6536\u9304\u65bc 2001 \u5e74 11 \u6708 \u81f3 2002 \u5e74 12 \u6708\u671f\u9593\u7684\u8a9e\u6599\u4f5c\u70ba\u6700\u5c0f\u5316\u97f3\u7d20\u932f\u8aa4(Minimum Phone Error, MPE)\u8072 \u5b78\u6a21\u578b\u8a13\u7df4\u7684\u8a9e\u6599\u4f86\u5efa\u7acb\u8072\u5b78\u6a21\u578b(Acoustic Models)[16]\u3002\u672c\u8ad6\u6587\u4ee5 2003 \u5e74\u6240\u8490 \u96c6\u7684\u8a9e\u6599\u4e2d\u6311\u9078\u7d04 1.5 \u500b\u5c0f\u6642\uff0c\u5305\u542b 292 \u53e5\u8a9e\u53e5\u3002 \u5728\u8a9e\u8a00\u6a21\u578b\u7684\u4f30\u6e2c\u4e0a\uff0c\u6211\u5011\u4f7f\u7528\u81ea 2001 \u81f3 2002 \u5e74\u4e2d\u592e\u901a\u8a0a\u793e(Central News Agency, CNA)\u7684\u6587\u5b57\u65b0\u805e\u8a9e\u6599\uff0c\u5167\u542b\u6709\u7d04\u4e00\u5104\u4e94\u5343\u842c\u500b\u4e2d\u6587\u5b57(\u7d93\u7531\u65b7\u8a5e\u4e4b\u5f8c\u7d04",
"num": null
}
}
}
} |