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{
"paper_id": "O15-1026",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:10:03.588508Z"
},
"title": "A Python Implementation of Automatic Speech-text Synchronization Using Speech Recognition and Text-to-Speech Technology",
"authors": [
{
"first": "Chun-Han",
"middle": [],
"last": "\u8cf4\u4fca\u7ff0",
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{
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"middle": [],
"last": "Lai",
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"institution": "Chang Gung University",
"location": {}
},
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{
"first": "Chao-Kai",
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"institution": "Chang Gung University",
"location": {}
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{
"first": "",
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"last": "Chang",
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"affiliation": {
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"institution": "Chang Gung University",
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{
"first": "Renyuan",
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"last": "\u5442\u4ec1\u5712",
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"institution": "Chang Gung University",
"location": {}
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"email": "renyuan.lyu@gmail.com"
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{
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"last": "Lyu",
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"institution": "Chang Gung University",
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"abstract": "\u672c\u7814\u7a76\u8a2d\u8a08\u4e00\u500b\u65b9\u4fbf\u8655\u7406\u6709\u8072\u66f8\u97f3\u6587\u540c\u6b65\u7684\u6280\u8853\uff0c\u5229\u7528\u96f2\u7aef\u7684\u6587\u5b57\u8f49\u8a9e\u97f3(Text-to-speech)\u6280\u8853\uff0c \u7d50\u5408\u8a9e\u97f3\u8fa8\u8b58(Speech Recognition)\u6280\u8853\uff0c\u8b93\u4f7f\u7528\u8005\u80fd\u5920\u4f7f\u7528\u81ea\u884c\u6e96\u5099\u7684\u6587\u7ae0\u4f86\u88fd\u4f5c\u81ea\u5df1\u7684\u300e\u8ddf \u8ff0\u7df4\u7fd2\u300f(Shadowing technique)\u7684\u5b78\u7fd2\u7d20\u6750\uff0c\u88fd\u4f5c\u9054\u5230\u8a5e\u5c64\u7d1a(Word-level)\u7684\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u3002 \u6b64\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u662f\u85c9\u7531\u300e\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u300f (Timed-text)\u6a94\u6848\u6240\u88fd\u4f5c\uff0c\u800c\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u5247 \u662f\u7531\u4f7f\u7528\u8005\u6240\u63d0\u4f9b\u7684\u6587\u7ae0\u9023\u540c\u5c0d\u61c9\u7684\u8a9e\u97f3\u8072\u6ce2\u6a94\u6848\uff0c\u7d93\u7531\u4e00\u5957\u540d\u70ba CGUAlign \u7684\u97f3\u6587\u540c\u6b65\u6280 \u8853\u4e4b\u8655\u7406\u6240\u7522\u751f\u7684\u3002 CGUAlign \u662f\u904b\u7528 Python \u5c07\u4e00\u6709\u540d\u7684\u8a9e\u97f3\u8fa8\u8b58\u6280\u8853\u2500HTK(Hidden Markov Model Toolkit) \u5305\u88dd\uff0c\u53ea\u8981\u63d0\u4f9b\u6587\u5b57\u6a94\u53ca\u5176\u6717\u8b80\u7684\u8a9e\u97f3\u6a94\uff0c\u5176\u4e2d\u8a9e\u97f3\u6a94\u662f\u7d93\u7531\u96f2\u7aef\u8a9e\u97f3\u5408\u6210\u6280 \u8853\u800c\u5f97\u4f86\u7684\uff0c\u5373\u80fd\u88fd\u4f5c\u51fa\u97f3\u6587\u540c\u6b65\u7684\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u6a94\u6848\uff0c\u96a8\u5f8c\uff0c\u6211\u5011\u4e5f\u5efa\u7acb\u4e00\u500b\u7c21\u6613\u7684\u4ee5 JavaScript \u88fd\u4f5c\u7684\u7db2\u7ad9\uff0c\u80fd\u5920\u904b\u7528\u9019\u500b\u6a94\u6848\u505a\u96fb\u8166\u8f14\u52a9\u8a9e\u8a00\u5b78\u7fd2(Computer-assisted language learning, CALL)\u4e4b\u7528\uff0c\u6b64\u7db2\u7ad9\u80fd\u5920\u95b1\u8b80\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\uff0c\u8b93\u4f7f\u7528\u8005\u80fd\u5920\u8f03\u8f15\u9b06\u7684\u505a\u8ddf\u8ff0\u7df4\u7fd2\uff0c \u6700\u5f8c\u6211\u5011\u4e5f\u63d0\u4f9b\u5373\u6642\u7ffb\u8b6f\u7684\u529f\u80fd\u4f86\u9054\u5230\u96fb\u8166\u8f14\u52a9\u8a9e\u8a00\u5b78\u7fd2\u7684\u76ee\u6a19\u3002",
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"abstract": [
{
"text": "\u672c\u7814\u7a76\u8a2d\u8a08\u4e00\u500b\u65b9\u4fbf\u8655\u7406\u6709\u8072\u66f8\u97f3\u6587\u540c\u6b65\u7684\u6280\u8853\uff0c\u5229\u7528\u96f2\u7aef\u7684\u6587\u5b57\u8f49\u8a9e\u97f3(Text-to-speech)\u6280\u8853\uff0c \u7d50\u5408\u8a9e\u97f3\u8fa8\u8b58(Speech Recognition)\u6280\u8853\uff0c\u8b93\u4f7f\u7528\u8005\u80fd\u5920\u4f7f\u7528\u81ea\u884c\u6e96\u5099\u7684\u6587\u7ae0\u4f86\u88fd\u4f5c\u81ea\u5df1\u7684\u300e\u8ddf \u8ff0\u7df4\u7fd2\u300f(Shadowing technique)\u7684\u5b78\u7fd2\u7d20\u6750\uff0c\u88fd\u4f5c\u9054\u5230\u8a5e\u5c64\u7d1a(Word-level)\u7684\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u3002 \u6b64\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u662f\u85c9\u7531\u300e\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u300f (Timed-text)\u6a94\u6848\u6240\u88fd\u4f5c\uff0c\u800c\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u5247 \u662f\u7531\u4f7f\u7528\u8005\u6240\u63d0\u4f9b\u7684\u6587\u7ae0\u9023\u540c\u5c0d\u61c9\u7684\u8a9e\u97f3\u8072\u6ce2\u6a94\u6848\uff0c\u7d93\u7531\u4e00\u5957\u540d\u70ba CGUAlign \u7684\u97f3\u6587\u540c\u6b65\u6280 \u8853\u4e4b\u8655\u7406\u6240\u7522\u751f\u7684\u3002 CGUAlign \u662f\u904b\u7528 Python \u5c07\u4e00\u6709\u540d\u7684\u8a9e\u97f3\u8fa8\u8b58\u6280\u8853\u2500HTK(Hidden Markov Model Toolkit) \u5305\u88dd\uff0c\u53ea\u8981\u63d0\u4f9b\u6587\u5b57\u6a94\u53ca\u5176\u6717\u8b80\u7684\u8a9e\u97f3\u6a94\uff0c\u5176\u4e2d\u8a9e\u97f3\u6a94\u662f\u7d93\u7531\u96f2\u7aef\u8a9e\u97f3\u5408\u6210\u6280 \u8853\u800c\u5f97\u4f86\u7684\uff0c\u5373\u80fd\u88fd\u4f5c\u51fa\u97f3\u6587\u540c\u6b65\u7684\u5e36\u6642\u9593\u9ede\u7684\u6587\u5b57\u6a94\u6848\uff0c\u96a8\u5f8c\uff0c\u6211\u5011\u4e5f\u5efa\u7acb\u4e00\u500b\u7c21\u6613\u7684\u4ee5 JavaScript \u88fd\u4f5c\u7684\u7db2\u7ad9\uff0c\u80fd\u5920\u904b\u7528\u9019\u500b\u6a94\u6848\u505a\u96fb\u8166\u8f14\u52a9\u8a9e\u8a00\u5b78\u7fd2(Computer-assisted language learning, CALL)\u4e4b\u7528\uff0c\u6b64\u7db2\u7ad9\u80fd\u5920\u95b1\u8b80\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\uff0c\u8b93\u4f7f\u7528\u8005\u80fd\u5920\u8f03\u8f15\u9b06\u7684\u505a\u8ddf\u8ff0\u7df4\u7fd2\uff0c \u6700\u5f8c\u6211\u5011\u4e5f\u63d0\u4f9b\u5373\u6642\u7ffb\u8b6f\u7684\u529f\u80fd\u4f86\u9054\u5230\u96fb\u8166\u8f14\u52a9\u8a9e\u8a00\u5b78\u7fd2\u7684\u76ee\u6a19\u3002",
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"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "\"\u53e5\u865f\"(\".\",\"\u3002\") \u3001\"\u554f\u865f\"(\"?\",\"\uff1f\") \u3001\" \u9a5a\u5606\u865f\"(\"!\",\"\uff01\") \u3001 \"\u7834\u6298\u865f\"(\"-\",\"\u2500\") \u3001\"\u5192\u865f\"(\":\",\"\uff1a\") \u3001\"\u9017\u865f\"(\",\",\"\uff0c\")\u3002",
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{
"text": "Step2:\u82e5\u6700\u7d42\u5b57\u4e32\u9577\u5ea6\u9084\u662f\u6709\u8d85\u904e 100 \u7684\uff0c\u5247\u6703\u5f9e\u8d85\u904e 100 \u7684\u5b57\u4e32\u4ee5\u4e2d\u9593\u7684\"\u7a7a\u767d\" \u5207\u5272\u3002 </span><span id=\"152\" class=\"normalLrcClass\" page=\"2\" time=\"103.410\" entence=\"0\" style=\"background: yellow;\">our </span><span id=\"153\" class=\"normalLrcClass\" page=\"2\" time=\"103.640\" entence=\"0\" style=\"background: yellow;\">talk </span><span id=\"154\" class=\"normalLrcClass\" page=\"2\" time=\"104.480\" entence=\"0\" style=\"background: yellow;\">turns </span><span id=\"155\" class=\"normalLrcClass\" page=\"2\" time=\"104.900\" entence=\"0\" style=\"background: yellow;\">to </span><span id=\"156\" class=\"normalLrcClass\" page=\"2\" time=\"105.080\" entence=\"0\" style=\"background: yellow;\">our </span><span id=\"157\" class=\"normalLrcClass\" page=\"2\" time=\"105.270\" entence=\"0\" style=\"background: yellow;\">children </span><span id=\"158\" class=\"normalLrcClass\" page=\"2\" time=\"106.040\" entence=\"0\" style=\"background: yellow;\">and </span><span id=\"159\" class=\"normalLrcClass\" page=\"2\" time=\"106.230\" entence=\"0\" style=\"background: yellow;\">our </span><span id=\"160\" class=\"normalLrcClass\" page=\"2\" time=\"106.410\" entence=\"0\" style=\"background: yellow;\">families </span><span id=\"161\" class=\"normalLrcClass\" page=\"2\" time=\"108.320\" entence=\"0\" style=\"background: yellow;\">however </span><span id=\"162\" class=\"normalLrcClass\" page=\"2\" time=\"108.770\" entence=\"0\" style=\"background: yellow;\">different </span><span id=\"163\" class=\"normalLrcClass\" page=\"2\" time=\"109.460\" entence=\"0\" style=\"background: yellow;\">we ",
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"bib_entries": {
"BIBREF1": {
"ref_id": "b1",
"title": "The HTK Book version 3, Microsoft Corporation",
"authors": [
{
"first": "Steve",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Steve Young, The HTK Book version 3, Microsoft Corporation, 2000. http://htk.eng.cam.ac.uk/",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "SailAlign: Robust long speech-text alignment",
"authors": [
{
"first": "A",
"middle": [],
"last": "Katsamanis",
"suffix": ""
},
{
"first": "M",
"middle": [
"P"
],
"last": "Black",
"suffix": ""
},
{
"first": "P",
"middle": [
"G"
],
"last": "Georgiou",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Goldstein",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Narayanan",
"suffix": ""
}
],
"year": 2011,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Katsamanis, M. P. Black, P. G. Georgiou, L. Goldstein, S. Narayanan\"SailAlign: Robust long speech-text alignment\" University of Southern California, Los Angeles, CA, USA , Jan. 28-31, 2011.",
"links": null
}
},
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"FIGREF0": {
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"text": "Thank you very much, Gertrude Mongella, for your dedicated work that has brought us to this point, distinguished delegates, and guests: 0:0:0.000000,0:0:1.619000Thank you very much, 0:0:1.619000,0:0:3.022000 Gertrude Mongella, 0:0:3.022000,0:0:6.549000 for your dedicated work that has brought us to this point,"
},
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"text": "FFmpeg\u3002 def ffmpeg_AudioDuration(filename): os.system(\"ffmpeg -report -y -i ./TTS-MP3/{0}.mp3\" + \"./FFmpeg-WAV/{1}.wav\".format(filename,filename)) dirlist= os.listdir() for i in dirlist : if i.find('ffmpeg')!=-1 and i.find('.log') !=-1 : report_name= i break f=open(report_name,\"r\") for i in f: if i.find(\"Duration:\") != -1: duration= i.split(\" Duration: \")[1].split(\",\")[0] hour= int(duration.split(\":\")[0]) min = int(duration.split(\":\")[1]) sec = float(duration.split(\":\")[2]) total_ms= int(hour* 3600000 + min*60000 + sec*1000) print(total_ms) f.close() os.system(\"copy \"+report_name+\" .\\\\FFmpeg-WAV\\\\\"+report_name) os.system(\"del \"+report_name) return total_ms \u5716\u516b\u3001Audio Converter \u7bc4\u4f8b\u7a0b\u5f0f\u78bc (\u4e8c) CGUAlign \u8a9e\u97f3\u8fa8\u8b58-Force Alignment \u672c\u7ae0\u7bc0\u5c07\u8aaa\u660e\u5982\u4f55\u5c07( \u4e00 ) \u96f2 \u7aef \u8a9e \u97f3 \u5408 \u6210 (Text-to-speech,TTS) \u5f97\u5230\u7684\u53e5\u5c64\u7d1a"
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"content": "<table><tr><td>\u81ea\u6211\u5167\u5316\u5b78\u7fd2\u53ca\u589e\u9032\u8a9e\u8a00\u80fd\u529b\u3002 \u9664\u6b64\u4e4b\u5916\uff0cGoogle Translate \u4e5f\u63d0\u4f9b\u6717\u8b80\u7684\u529f\u80fd\uff0c\u5373\u6587\u5b57\u8f49\u8a9e\u97f3(Text-to-speech)\u7684\u4eba (\u56db) CGUAlign[3]</td></tr><tr><td>\u4e8c\u3001\u76f8\u95dc\u7814\u7a76 \u5de5\u8a9e\u97f3\u5408\u6210\u6717\u8b80\u7684\u529f\u80fd\uff0c\u53e6\u5916\u4e5f\u63d0\u4f9b\u67e5\u8a62\u6587\u5b57\u62fc\u97f3\u7684\u529f\u80fd\uff0c\u5373\u80fd\u5920\u63d0\u4f9b\u975e\u62fc\u97f3\u8a9e\u8a00 \u7684\u7f85\u99ac\u62fc\u97f3\u67e5\u8a62\u3002 \u4e09\u3001\u7814\u7a76\u65b9\u6cd5 CguAlign \u662f\u6a21\u4eff[4]\u4ee5 \u904b\u7528\u7684\u4e00\u5957\u6280\u8853\uff0c\u70ba\u672c\u5be6\u9a57\u5ba4\u4e00\u500b\u65b9\u4fbf\u8655\u7406\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u7684\u6280\u8853\uff0c\u539f\u672c\u662f\u70ba\u4e86\u5c07 (\u4e00) \u8ddf\u8ff0\u7df4\u7fd2(Shadowing Technique) Google Translate \u6240\u63d0\u4f9b\u7684\u4e09\u7a2e\u529f\u80fd\u2500\u7ffb\u8b6f\u3001\u6717\u8b80\u3001\u62fc\u97f3\u5df2\u7d93\u5f88\u9069\u5408\u505a\u521d\u6b65\u7684\u8a9e\u8a00 \u672c\u7ae0\u7bc0\u5167\u5bb9\u65e8\u5728\u4ecb\u7d39\u6574\u9ad4\u7684\u7814\u7a76\u65b9\u6cd5\uff0c\u5168\u7ae0\u5206\u70ba\u4e09\u7bc0\uff0c \u5f9e Youtube \u4e0a\u6240\u53d6\u5f97\u7684\u53e5\u5c64\u7d1a Timed-text sbv \u6a94\uff0c\u4ee5\u7a0b\u5f0f\u81ea\u52d5\u5207\u97f3\u53d6\u4ee3\u50b3\u7d71\u4eba\u5de5\u624b (\u4e00) \u96f2\u7aef\u8a9e\u97f3\u5408\u6210(Text-to-speech,TTS) Shadowing technique \u662f\u4e00\u7a2e\u8a9e\u8a00\u5b78\u7fd2\u6280\u5de7\uff0c\u4e00\u822c\u6211\u5011\u7a31\u4e4b\u70ba\u8ddf\u8ff0\u7df4\u7fd2\u6216\u8005\u662f\u5f71\u5b50\u7df4 \u7fd2\uff0c\u8207\u76ee\u524d\u53f0\u7063\u6240\u5e38\u898b\u7684\u8b1b\u8ff0\u5f0f\u6559\u5b78\u6cd5\u4e0d\u540c\uff0c\u5b83\u6bd4\u8f03\u76f8\u4f3c\u65bc\u6240\u8b02\u7684\u807d\u8aaa\u5f0f\u6559\u5b78\u6cd5\uff0c \u5b78\u7fd2\uff0c\u4f46\u662f\u5176\u9802\u591a\u53ea\u80fd\u88fd\u4f5c\u51fa\u53e5\u5c64\u7d1a(Sentence-level)\u7684\u6548\u679c\uff0c\u5176\u53e5\u5c64\u7d1a\u662f\u6307\u5728\u97f3\u6587 \u52d5\u7684\u65b9\u5f0f\u5207\u97f3\u6210\u8a5e\u5c64\u7d1a Timed-text \u6a94\u7684\u65b9\u6cd5\uff0c\u6b64\u65b9\u6cd5\u9664\u4e86\u53ef\u4ee5\u6e1b\u5c11\u4eba\u529b\u8cc7\u6e90\uff0c\u9084 (\u4e8c) CGUAlign \u8a9e\u97f3\u8fa8\u8b58-ForceAlignment</td></tr><tr><td>\u4f46\u5176\u53c8\u8207\u807d\u8aaa\u5f0f\u6559\u5b78\u6cd5\u6709\u4e00\u9ede\u9ede\u4e0d\u540c\uff0c\u8ddf\u8ff0\u7df4\u7fd2\u8207\u807d\u8aaa\u6559\u5b78\u6cd5\u8f03\u70ba\u4e0d\u540c\u7684\u5730\u65b9\u5728\u65bc \u540c\u6b65\u64ad\u653e\u6642\u7576\u4e0b\u8a9e\u97f3\u5167\u5bb9\u662f\u4ee5\u53e5\u5b50\u70ba\u55ae\u4f4d\u7684\u986f\u793a\u65bc\u756b\u9762\u4e2d\u3002\u7531\u800c\u672c\u7814\u7a76\u5247\u662f\u9032\u4e00\u6b65 (\u4e09) \u7db2\u7ad9\u5448\u73fe(Website presentation) \u80fd\u5920\u5927\u5e45\u6e1b\u5c11\u4eba\u5de5\u624b\u52d5\u5207\u97f3\u6240\u6d6a\u8cbb\u7684\u6642\u9593\u3002\u53ea\u9700\u8f38\u5165\u6587\u5b57\u6a94\u4ee5\u53ca\u8072\u97f3\u6a94\uff0c\u7d93\u904e\u97f3\u6587 \u807d\u8aaa\u6559\u5b78\u662f\u6559\u5e2b\u4ee5\u81ea\u8eab\u7684\u6f14\u7e79\u53e3\u8aaa\u5167\u5bb9\u4f86\u4fc3\u4f7f\u5b78\u751f\u53cd\u8986\u7df4\u7fd2\u8a9e\u8a00\u5167\u5bb9\u800c\u8ddf\u8ff0\u7df4\u7fd2 \u6bd4\u8f03\u50be\u5411\u65bc\u5b78\u7fd2\u8005\u81ea\u4e3b\u8a13\u7df4\u7684\u65b9\u5f0f\u3002 \u5229\u7528\u9019\u4e09\u7a2e\u529f\u80fd\uff0c\u5229\u7528\u62fc\u97f3\u548c\u4eba\u5de5\u8a9e\u97f3\u5408\u6210\u7684\u529f\u80fd\u80fd\u5920\u88fd\u4f5c\u51fa Google Translate \u6240 \u7121\u6cd5\u9054\u5230\u7684\u8a5e\u5c64\u7d1a(Word-level)\u7684\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\uff0c\u5176\u8a5e\u5c64\u7d1a\u662f\u6307\u5728\u97f3\u6587\u540c\u6b65\u64ad\u653e \u5c0d\u9f4a\u7684\u8655\u7406\uff0c\u5373\u53ef\u5f97\u5230\u5e36\u6709\u6642\u9593\u9ede\u7684 Timed-text \u6587\u5b57\u6a94\u6848\u3002\u4f46\u6b64\u6280\u8853\u7121\u6cd5\u8655\u7406\u904e \u5716\u4e09\u70ba\u7cfb\u7d71\u6574\u9ad4\u6d41\u7a0b\u5716\uff0c\u539f\u59cb\u6587\u5b57\u6a94\u7d93\u7531(\u4e00)\u6703\u5f97\u5230\u53e5\u5c64\u7d1a\u7684\u5e36\u6709\u6642\u9593\u9ede\u7684\u6587\u5b57\u7684</td></tr><tr><td>\u8a9e\u8a00\u5b78\u7fd2\u8005\u8ddf\u8ff0\u7684\u5b78\u7fd2\u5c0d\u8c61\u4e0d\u4e00\u5b9a\u70ba\u771f\u4eba\uff0c\u4e5f\u53ef\u80fd\u50c5\u662f\u4e00\u500b\u8a9e\u97f3\u6216\u5f71\u50cf\u6a94\uff0c\u5728\u8ddf\u8ff0 \u6642\u7576\u4e0b\u7684\u8a9e\u97f3\u5167\u5bb9\u9664\u4e86\u4ee5\u53e5\u5b50\u70ba\u55ae\u4f4d\u7684\u986f\u793a\u65bc\u756b\u9762\u4e2d\u4e26\u9644\u52a0\u53e5\u5b50\u4e2d\u6bcf\u500b\u8a5e\u7684\u986f\u793a \u9577\u7684\u8072\u97f3\u6a94\uff0c\u56e0\u6b64\u5e0c\u671b\u7ad9\u5728\u96f2\u7aef\u8a9e\u97f3\u5408\u6210\u6280\u8853\u4e0a\uff0c\u5c07\u4ee5\"\u53e5\"\u70ba\u5c64\u7d1a\u7684 TTS \u6539\u826f\uff0c\u4f7f \u6a94\u6848\uff0c\u518d\u7d93\u7531(\u4e8c)\u6703\u5f97\u5230\u8a5e\u5c64\u7d1a\u7684\u5e36\u6709\u6642\u9593\u9ede\u7684\u6587\u5b57\u7684\u6a94\u6848\uff0c\u6700\u5f8c\u7d93\u7531(\u4e09)\u80fd\u5920\u4ee5</td></tr><tr><td>\u7684\u904e\u7a0b\u4e2d\uff0c\u8ddf\u8ff0\u8005\u4ee5\u81ea\u6211\u6240\u80fd\u7684\u767c\u97f3\u6280\u5de7\u4ee5\u53ca\u95b1\u8b80\u80fd\u529b\u53bb\u76e1\u53ef\u80fd\u5730\u6a21\u4eff\u6240\u8981\u5b78\u7fd2\u7684 \u6548\u679c\uff0c\u800c\u8a5e\u5c64\u7d1a\u7684\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u80fd\u5920\u8b93\u5b78\u7fd2\u8005\u66f4\u5bb9\u6613\u7684\u8033\u807d\u3001\u773c\u770b\u4f86\u505a\u8ddf\u8ff0\u6280\u5de7 \u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\u7684\u65b9\u5f0f\u700f\u89bd\u6b64\u5e36\u6709\u6642\u9593\u9ede\u7684\u6587\u5b57\u7684\u6a94\u6848\uff0c\u4e26\u4ee5\u6b64\u505a\u4e00\u500b\u96fb\u8166\u8f14\u52a9\u8a9e \u5176\u80fd\u5920\u9054\u5230\"\u5b57\"\u7684\u5c64\u7d1a\u3002\u5716\u4e8c\u70ba CGUAlign \u4e4b\u6d41\u7a0b\u5716\u3002 \u8a9e\u8a00\u5c0d\u8c61\u6216\u8005\u662f\u5f71\u97f3\u5167\u5bb9\uff0c\u9019\u7a2e\u5b78\u7fd2\u65b9\u5f0f\u6709\u5982\u9e1a\u9d61\u5b78\u820c\uff0c\u662f\u4e00\u7a2e\u53cd\u8986\u7df4\u7fd2\u4ee5\u53ca\u81ea\u6211 \u5167\u5316\u7684\u904e\u7a0b\uff0c\u5728\u5176\u4ed6\u7684\u7814\u7a76\u4e2d[1]\u6211\u5011\u4e5f\u53ef\u4ee5\u767c\u73fe\u5230\u5229\u7528\u9019\u6a23\u7684\u8a9e\u8a00\u5b78\u7fd2\u6280\u5de7\u662f\u4e00 \u7684\u7df4\u7fd2\u3002 \u5716\u4e8c.a\u3001\u5207\u5230\u53e5\u7684 sbv \u6a94 \u8a00\u5b78\u7fd2\u7684\u52d5\u4f5c\u3002</td></tr><tr><td>\u7a2e\u5feb\u901f\u5167\u5316\u65b9\u5f0f\u53bb\u5b78\u7fd2\u4e00\u7a2e\u8a9e\u8a00\u7684\u65b9\u6cd5\u3002 (\u4e09) HTK (Hidden Markov Model Toolkit)[2]</td></tr><tr><td>HTK \u7684\u5168\u540d\u70ba Hidden Markov Model Toolkit\uff0c\u662f\u4e00\u5957\u61c9\u7528\u65bc\u8a9e\u97f3\u8a13\u7df4\u8207\u8fa8\u8b58\u7684\u514d (\u4e8c) Google Translate \u73fe\u5728 Google \u5728\u8a31\u591a\u65b9\u9762\u5ee3\u6cdb\u5730\u88ab\u4f7f\u7528\uff0c\u4e0d\u53ea\u662f\u5728\u641c\u5c0b\u5f15\u64ce\u7684\u529f\u80fd\u4e0a\uff0c\u8a31\u591a\u4eba\u5728\u9047 \u5230\u8a9e\u8a00\u4e0a\u554f\u984c\u7684\u6642\u5019\uff0c\u5f80\u5f80\u6703\u85c9\u7531 Google \u6240\u63d0\u4f9b\u7684\u7ffb\u8b6f\u529f\u80fd\u2500 Google Translate \u8cbb\u8edf\u9ad4\u3002HTK \u65bc 1989 \u5e74\u958b\u59cb\u7531\u82f1\u570b\u528d\u6a4b\u5927\u5b78\u5de5\u7a0b\u7cfb (Cambridge University \u5716\u56db\u3001\u96f2\u7aef\u8a9e\u97f3\u5408\u6210\u6d41\u7a0b\u5716 Engineering Department, CUED)\u7684\u6a5f\u5668\u667a\u80fd\u5be6\u9a57\u5ba4 (Machine Intelligence Lab\uff0c\u6216\u662f \u5927\u773e\u8f03\u70ba\u719f\u6089\u7684 Speech Vision and Robotics Group)\u9032\u884c\u958b\u767c\uff0c\u8a72\u5718\u968a\u5229\u7528\u96b1\u85cf\u5f0f\u99ac 1. \u6587\u5b57\u5207\u5272</td></tr><tr><td>\u5e6b\u5fd9\u3002 Google Translate \u6240\u63d0\u4f9b\u7684\u7ffb\u8b6f\u529f\u80fd\u975e\u5e38\u5f37\u5927\uff0c\u63d0\u4f9b\u8fd1\u767e\u7a2e\u7684\u8a9e\u8a00\u76f8\u4e92\u7684\u7ffb \u53ef\u592b\u6a21\u578b (HMM)\u5efa\u9020\u51fa\u4e00\u5957 HMM-based \u7684\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u30021999 \u5e74\u5341\u4e00\u6708\uff0c\u5fae\u8edf</td></tr><tr><td>\u8b6f\uff0c\u800c\u4e14\u5728\u53d6\u5f97\u6b64\u529f\u80fd\u7684\u4fbf\u5229\u6027\u4e0a\uff0c\u4e5f\u662f\u7121\u8207\u502b\u6bd4\uff0c\u64da Google \u7d71\u8a08\uff0c\u81f3 2015 \u5e74 6 \u8cfc\u5165\u64c1\u6709\u6b64\u8edf\u9ad4\u7684 Entropic \u516c\u53f8\uff0c\u4e26\u65bc\u7fcc\u5e74\u5c07 HTK \u5b9a\u4f4d\u70ba\u514d\u8cbb\u8edf\u9ad4\uff0c\u671f\u671b HTK \u56e0 Google Translate TTS \u7121\u6cd5\u76f4\u63a5\u8f38\u5165\u9577\u5ea6\u5927\u65bc 100 \u7684\u5b57\u4e32\uff0c\u56e0\u6b64\u9700\u8981\u5148\u505a\u6587\u5b57\u5206</td></tr><tr><td>\u6708 Google Translate \u6bcf\u5929\u9700\u8981\u8655\u7406\u8d85\u904e 1000 \u5104\u7b46\u5b57\u8a5e\u3002 \u5716\u4e00\u662f Google Translate \u7684\u4f7f\u7528\u4ecb\u9762\uff0c\u5176\u63d0\u4f9b\u7684\u5373\u6642\u7ffb\u8b6f\u529f\u80fd\uff0c\u8b93\u4f7f\u7528\u8005\u53ef\u4ee5\u5728\u5de6 \u4f5c\u70ba\u8a9e\u97f3\u8fa8\u8b58\u7684\u5171\u540c\u5e73\u53f0\uff0c\u4fbf\u80fd\u8c50\u5bcc HTK \u7684\u529f\u80fd\u6027\uff0c\u4ee5\u53ca\u63d0\u5347\u8a9e\u97f3\u8fa8\u8b58\u7b49\u76f8\u95dc\u6280 \u5272\uff0c\u5c07\u5176\u9577\u5ea6\u964d\u4f4e\u65bc\u5c0f\u65bc 100\uff0c\u4e26\u7a31\u6b64\u70ba\u53e5\u5c64\u7d1a\u7684\u7d14\u6587\u5b57\u6a94\uff0c\u57fa\u672c\u7684\u5207\u5272\u65b9\u6cd5\u53ea\u5148 \u8853\u3002\u70ba\u4e86\u9054\u5230\u9019\u500b\u76ee\u6a19\uff0cHTK \u5efa\u7f6e\u5b98\u65b9\u7db2\u7ad9\uff0c\u4ee5\u63d0\u4f9b\u958b\u653e\u7684\u5b8c\u6574\u529f\u80fd\u539f\u59cb\u78bc\u53ca\u8aaa \u6309\u7167\u6a19\u9ede\u7b26\u865f\u4f5c\u5207\u5272\u3002 \u660e\u66f8\u3002 Step1:\u6309\u7167\u6a19\u9ede\u7b26\u865f\u505a\u5207\u5272\u4f8b\u5982:</td></tr><tr><td>\u908a\u7684\u8f38\u5165\u6b04\u4f4d\u8f38\u5165\u6587\u5b57\uff0c\u7ffb\u8b6f\u7d50\u679c\u6703\u5373\u6642\u5728\u53f3\u908a\u7684\u7d50\u679c\u6846\u986f\u793a\uff0c\u5c07\u6ed1\u9f20\u9f20\u6a19\u79fb\u5230\u7ffb \u4e00\u3001\u7dd2\u8ad6 \u96a8\u8457\u5730\u7403\u6751\u7684\u8da8\u52e2\u4f86\u81e8\uff0c \u300c\u8a9e\u8a00\u5b78\u7fd2\u300d \u662f\u73fe\u4eca\u793e\u6703\u666e\u7f85\u5927\u773e\u6240\u9700\u8981\u9762\u81e8\u7684\u4e00\u9805\u8ab2\u984c\uff0c \u8b6f\u7d50\u679c\u6587\u5b57\u4e0a\u53ef\u4ee5\u770b\u5230\u5176\u5c0d\u61c9\u7684\u539f\u6587\uff0c\u5716\u4e00\u7bc4\u4f8b\u70ba\u5c07\u4e2d\u6587\u7ffb\u8b6f\u6210\u82f1\u6587\u7684\u7bc4\u4f8b\u3002 \u7531\u65bc\u8a9e\u97f3\u8fa8\u8b58\u7684\u539f\u7406\u5305\u542b\u76f8\u7576\u9ad8\u6df1\u7684\u6578\u5b78\uff0c\u76f8\u5c0d\u5730\u4f7f\u5f97\u7a0b\u5f0f\u78bc\u4e5f\u4e0d\u6613\u64b0\u5beb\uff0c\u9020\u6210\u9032 \u5165\u9580\u6abb\u9ad8\uff0c\u8907\u96dc\u5ea6\u4e0d\u6613\u638c\u63a7\u7684\u60c5\u6cc1\u7522\u751f\u3002\u4f46\u81ea\u5f9e HTK \u5728 2000 \u5e74\u5b9a\u4f4d\u6210\u958b\u653e\u539f\u59cb\u78bc \u5716\u4e8c.b\u3001trs \u6a94</td></tr><tr><td>\u7684\u514d\u8cbb\u8edf\u9ad4\u5f8c\uff0c\u5927\u5e45\u964d\u4f4e\u4e86\u9032\u5165\u9580\u6abb\uff0c\u4e26\u52a0\u901f\u63d0\u6607\u8a9e\u97f3\u6280\u8853\u7684\u767c\u5c55\uff0c\u7d9c\u89c0\u76ee\u524d\u570b\u5167 \u4e5f\u662f\u4e00\u7a2e\u8da8\u52e2\uff0c\u56e0\u6b64\u57f9\u990a\uf97c\u597d\u7684\u591a\u570b\u8a9e\u8a00\u80fd\u529b\uff0c\u5df2\u6210\u70ba\u7576\u4eca\u793e\u6703\u4e0d\u53ef\u6216\u7f3a\u7684\u76ee\u6a19\u3002 \u91dd\u5c0d\u65bc\u53f0\u7063\u4eba\u800c\u8a00\uff0c\u82f1\u8a9e\u5b78\u7fd2\u7684\u9700\u6c42\u66f4\u662f\u986f\u5f97\u6bd4\u5176\u4ed6\u8a9e\u8a00\u4f86\u5f97\u66f4\u70ba\u91cd\u8981\uff0c\u4e8b\u5be6\u4e0a\u6211 \u5916\u8a9e\u97f3\u6280\u8853\u76f8\u95dc\u7684\u5be6\u9a57\u5de5\u5177\u548c\u7cfb\u7d71\u958b\u767c\uff0c\u7d55\u5927\u90e8\u5206\u90fd\u4ee5 HTK \u70ba\u4e3b\u6d41\uff1b\u7531\u6b64\u53ef\u77e5\uff0c \u5716\u4e09\u3001\u7cfb\u7d71\u6d41\u7a0b\u5716</td></tr><tr><td>\u5011\u77e5\u9053\uff0c \u300c\u8a9e\u8a00\u5b78\u7fd2\u300d\u4e26\u975e\u53ea\u662f\u5982\u540c\u4e00\u822c\u8ab2\u7a0b\u7684\u5b78\u7fd2\uff0c\u53c8\u5206\u70ba\u300c\u807d\u300d \u3001 \u300c\u8aaa\u300d \u3001 \u300c\u8b80\u300d \u3001 HTK \u5728\u8a9e\u97f3\u6280\u8853\u7684\u7814\u7a76\u9818\u57df\u5360\u4e86\u4e0d\u53ef\u6216\u7f3a\u7684\u5730\u4f4d\u3002 \u5716\u4e8c\u3001CGUAlign \u6d41\u7a0b\u5716</td></tr><tr><td>\u300c\u5beb\u300d \uff0c\u5176\u9700\u8981\u7d93\u904e\u300c\u81ea\u6211\u5167\u5316\u300d \u3001 \u300c\u7df4\u7fd2\u300d \u3001 \u300c\u6f14\u7e79\u300d\u7b49\u904e\u7a0b\u624d\u80fd\u6839\u6df1\u8482\u56fa\u7684\u8a18\u61b6\u5728 (\u4e00) \u96f2\u7aef\u8a9e\u97f3\u5408\u6210(Text-to-speech,TTS)</td></tr><tr><td>\u6211\u5011\u8166\u6d77\u4e2d\uff0c\u800c\u5728\u820a\u6709\u7684\u81ea\u6211\u5b78\u7fd2\u4e2d\uff0c\u53c8\u7f3a\u4e4f\u7368\u7279\u7684\u8a9e\u8a00\u5b78\u7fd2\u74b0\u5883\uff0c\u7f3a\u4e4f\u7df4\u7fd2\u7684\u5c0d</td></tr><tr><td>\u8c61\uff0c\u5982\u679c\u8981\u8acb\u4ed6\u4eba\u4f86\u6307\u5c0e\u6559\u5b78\uff0c\u5f80\u5f80\u53c8\u6240\u8cbb\u4e0d\u8cb2\uff0c\u800c\u6578\u4f4d\u5316\u96f2\u7aef\u5b78\u7fd2\u5728\u7576\u4eca\u7684\u4e16\u4ee3 \u672c\u7bc0\u5c07\u8aaa\u660e\u5982\u4f55\u5c07\u7d14\u6587\u672c\u7d93\u7531\u6587\u5b57\u7684\u9810\u5148\u8655\u7406\uff0c\u900f\u904e Google Translate \u7684\u96f2\u7aef\u6587\u5b57 \u662f\u4e00\u500b\u71b1\u9580\u7684\u8da8\u52e2\uff0c\u5982\u300c\u8996\u8a0a\u6559\u5b78\u300d \u3001 \u300c\u7dda\u4e0a\u5b78\u7fd2\u300d \uff0c\u9019\u4e9b\u90fd\u662f\u7db2\u8def\u666e\u53ca\u8207\u8cc7\u8a0a\u767c\u5c55 \u8f49\u8a9e\u97f3(Text-to-speech,TTS)\u7684\u670d\u52d9\uff0c\u53d6\u5f97 TTS \u7684\u8a9e\u97f3\u6a94\uff0c\u4e26\u5c07\u6b64\u8a9e\u97f3\u6a94\u548c\u6587\u5b57\u6a94\u7d50 \u4e0b\u7684\u91cd\u8981\u7522\u7269\uff0c\u82e5\u6211\u5011\u53ef\u4ee5\u5229\u7528\u9069\u5ea6\u7684\u96fb\u8166\u56de\u994b\u7d50\u5408\u6578\u4f4d\u5316\u5b78\u7fd2\uff0c\u4e5f\u8a31\u80fd\u70ba\u66f4\u591a\u4f7f \u7528\u8005\u9020\u5c31\u4e00\u500b\u65b0\u5f62\u614b\u7684\u81ea\u6211\u5b78\u7fd2\u65b9\u5f0f\u3002 \u5408\u7522\u751f\u53e5\u5c64\u7d1a\u7684 Timed-text \u6a94\u6848\u4ee5\u4f9b\u4e0b\u4e00\u968e\u6bb5 CGUAlign \u4f7f\u7528\u3002</td></tr><tr><td>\u672c \u7814 \u7a76 \u8a2d \u8a08 \u4e00 \u500b \u65b9 \u4fbf \u8655 \u7406 \u6709 \u8072 \u66f8 \u97f3 \u6587 \u540c \u6b65 \u7684 \u6280 \u8853 \uff0c \u5229 \u7528 \u96f2 \u7aef \u7684 \u6587 \u5b57 \u8f49 \u8a9e \u97f3 \u5716\u4e00\u3001Google Translate \u7db2\u9801\u4ecb\u9762</td></tr><tr><td>(Text-to-speech)\u6280\u8853\uff0c\u7d50\u5408\u8a9e\u97f3\u8fa8\u8b58(Speech Recognition)\u6280\u8853\uff0c\u8b93\u4f7f\u7528\u8005\u80fd\u5920\u4f7f\u7528</td></tr><tr><td>\u81ea\u884c\u6e96\u5099\u7684\u6587\u672c\u4f86\u88fd\u4f5c\u81ea\u5df1\u7684\u8ddf\u8ff0\u7df4\u7fd2\u7684\u5b78\u7fd2\u7d20\u6750\uff0c\u88fd\u4f5c\u9054\u5230\u8a5e\u5c64\u7d1a(Word-level)</td></tr><tr><td>\u7684\u97f3\u6587\u540c\u6b65\u6709\u8072\u66f8\uff0c\u5176\u4e0d\u50c5\u53ef\u4ee5\u63d0\u4f9b\u97f3\u6587\u540c\u6b65\u7684\u96fb\u5b50\u66f8\u4f9b\u4f7f\u7528\u8005\u95b1\u8b80\u6587\u7ae0\uff0c\u4e5f\u53ef\u4ee5 \u8b93\u4f7f\u7528\u8005\u85c9\u7531\u6717\u8aa6\u6587\u7ae0\u7684\u65b9\u5f0f\uff0c\u4e26\u900f\u904e\u8ddf\u8ff0\u7df4\u7fd2\u7684\u5be6\u4f5c\u548c\u5373\u6642\u7ffb\u8b6f\u7684\u6548\u679c\uff0c\u4ee5\u9054\u5230 \u5716\u4e8c.c\u3001lrc \u6a94</td></tr></table>",
"text": "Perl \u5305\u88dd HTK \u7684\u65b9\u6cd5\uff0cCguAlign \u6539\u7528 Python \u5c07 HTK \u5305\u88dd\u3001"
},
"TABREF1": {
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td colspan=\"6\">Translate TTS Parameters \u5728\u6b64\u7bc4\u4f8b\u7a0b\u5f0f\u78bc\u4e2d\uff0c\u5148\u5c0d\u8a2d\u5b9a GOOGLE_TTS_URL \u8f38\u5165\u7db2\u5740\uff0c\u7528 payload \u8f38\u5165\u5c0d</td></tr><tr><td/><td colspan=\"6\">parameters Google Translate \u7684 TTS \u4e4b\u53c3\u6578\u5982\u4e0a\u8ff0\u8868\u4e00\u6240\u793a\uff0c\u6700\u5f8c\u904b\u7528 urllib.request.urlopen() \u610f\u7fa9</td></tr><tr><td/><td colspan=\"6\">tl \u767c\u51fa request \u53d6\u5f97\u53e5\u5b50\u5167\u5bb9\u7684 mp3 \u8a9e\u97f3\uff0c\u7136\u5f8c\u7528 read()\u8b80\u53d6 mp3 \u8a9e\u97f3\u7684\u5167\u5bb9\u4e26\u7528 Target Language\uff0c\u76ee\u6a19\u8a9e\u8a00\uff0c\u8868\u793a\u8981\u6587\u5b57 TTS \u7684\u8a9e\u8a00\u7a2e\u985e\u3002</td></tr><tr><td/><td colspan=\"6\">q len( )\u8a08\u7b97\u51fa\u53e5\u5b50 mp3 \u97f3\u8a0a\u5167\u5bb9\u7684\u9577\u5ea6\u3002 Query\uff0c\u6b32 TTS \u7684\u6587\u5b57\u3002</td></tr><tr><td/><td>total</td><td colspan=\"5\">Total number of text segments\uff0c\u6587\u7ae0\u5206\u6bb5\u7684\u500b\u6578\u3002</td></tr><tr><td/><td colspan=\"2\">idx 3. Creat Timed-text File</td><td colspan=\"4\">Index of text segments\uff0c\u6587\u7ae0\u5206\u6bb5\u7684\u6307\u6a19\u3002</td></tr><tr><td/><td>textlen</td><td colspan=\"5\">String length in this segment\uff0c\u6b64 Query \u7684\u5b57\u4e32\u9577\u5ea6\u3002</td></tr><tr><td/><td colspan=\"6\">\u5229\u7528\u4e0a\u4e00\u6b65\u9a5f\u6240\u8490\u96c6\u7684\u6bcf\u4e00\u500b segment \u7684 byteNum \u5927\u5c0f\uff0c\u4e26\u8a08\u7b97 byteNum \u7684\u7e3d\u548c\uff0c</td></tr><tr><td/><td colspan=\"6\">\u80fd \u5920 \u8a08 \u7b97 \u51fa \u6bcf \u4e00 \u6bb5 segment \u5728 \u7e3d \u8a9e \u97f3 \u9577 \u5ea6 \u4e2d \u7684 \u6642 \u9593 \u9577 \u5ea6 \u5176 \u516c \u5f0f \u5982 \u4e0b \uff0c</td></tr><tr><td>1 2</td><td colspan=\"6\">import urllib.request SegmentLength(i)\u70ba\u7b2c i \u6bb5\u8a9e\u97f3\u7684\u9577\u5ea6\uff0cByteNum(i)\u70ba\u7b2c i \u6bb5\u8a9e\u97f3\u7684\u6a94\u6848\u5927\u5c0f\uff0c import urllib.parse Sum(ByteNum)\u70ba\u7e3d\u5171\u7684\u6a94\u6848\u5927\u5c0f\uff0cTotalLength \u70ba\u7e3d\u8a9e\u97f3\u9577\u5ea6\u3002</td></tr><tr><td>3 4</td><td colspan=\"3\">savefile=\"./TTS.mp3\" f= open(savefile, 'wb+') Sum ByteNum ByteNum i i gth SegmentLen \uf03d ( ) ( ) (</td><td>)</td><td>\uf0b4</td><td>TotalLengt</td><td>h</td></tr><tr><td>5</td><td colspan=\"3\">\u6587\u5b57= \"Chung Gung University Student\"</td><td/><td/></tr><tr><td>6</td><td colspan=\"6\">GOOGLE_TTS_URL= 'https://translate.google.com.tw/translate_tts?'</td></tr><tr><td>7</td><td colspan=\"3\">payload = { 'ie': 'utf-8',</td><td/><td/></tr><tr><td>8</td><td colspan=\"3\">'tk': '308912',</td><td/><td/></tr><tr><td>9</td><td colspan=\"3\">'client': 't',</td><td/><td/></tr><tr><td>10</td><td colspan=\"2\">'tl': 'en',</td><td/><td/><td/></tr><tr><td>11</td><td colspan=\"2\">'q': \u6587\u5b57,</td><td/><td/><td/></tr><tr><td>12</td><td colspan=\"2\">'total': 1,</td><td/><td/><td/></tr><tr><td>13</td><td colspan=\"2\">'idx': 0,</td><td/><td/><td/></tr><tr><td>14</td><td colspan=\"3\">'textlen': len(text) }</td><td/><td/></tr><tr><td>15</td><td>try:</td><td/><td/><td/><td/></tr><tr><td>19</td><td colspan=\"3\">hdr = {r = urllib.request.urlopen(req)</td><td/><td/></tr><tr><td>20</td><td/><td/><td/><td/><td/></tr><tr><td>21</td><td/><td/><td/><td/><td/></tr><tr><td>22</td><td>byte= r.read()</td><td/><td/><td/><td/></tr><tr><td>23</td><td>f.write(byte)</td><td/><td/><td/><td/></tr><tr><td>24</td><td colspan=\"2\">byteNum= len(byte)</td><td/><td/><td/></tr><tr><td>25</td><td colspan=\"2\">except Exception as e:</td><td/><td/><td/></tr><tr><td>26</td><td>raise</td><td/><td/><td/><td/></tr><tr><td>27</td><td>f.close()</td><td/><td/><td/><td/></tr><tr><td/><td/><td colspan=\"5\">\u5716\u4e94\u3001Communicate to Google \u7bc4\u4f8b\u7a0b\u5f0f\u78bc</td></tr></table>",
"text": ""
},
"TABREF2": {
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td colspan=\"4\">\u4ee5\u53ca\u5176 parameter \u6539\u6210\u5982\u8868\u4e8c\uff0c\u5373\u53ef\u5f97\u5230\u6b64\u539f\u6587\u7684\u7f85\u99ac\u62fc\u97f3\u3002</td></tr><tr><td/><td colspan=\"3\">\u8868\u4e8c\u3001\u53d6\u5f97\u7f85\u99ac\u62fc\u97f3\u7684 parameters(\u4ee5\u4e2d\u6587\u70ba\u4f8b)</td></tr><tr><td>parameters</td><td>\u503c</td><td>parameters</td><td>\u503c</td></tr><tr><td>ie</td><td>UTF-8</td><td>kc</td><td>1</td></tr><tr><td>inputm</td><td>1</td><td>tk</td><td>520254|125262</td></tr><tr><td>oe</td><td>UTF-8</td><td>dt</td><td>bd</td></tr><tr><td>otf</td><td>1</td><td>dt</td><td>ex</td></tr><tr><td>trs</td><td>1</td><td>dt</td><td>ld</td></tr><tr><td>client</td><td>T</td><td>dt</td><td>md</td></tr><tr><td>sl</td><td>Zh-CN</td><td>dt</td><td>qca</td></tr><tr><td>hl</td><td>Zh-TW</td><td>dt</td><td>rw</td></tr><tr><td>rom</td><td>1</td><td>dt</td><td>rm</td></tr><tr><td>srcrom</td><td>1</td><td>dt</td><td>ss</td></tr><tr><td>ssel</td><td>0</td><td>dt</td><td>t</td></tr><tr><td>tsel</td><td>0</td><td>dt</td><td>at</td></tr><tr><td>tl</td><td>\u76ee\u6a19\u8a9e\u8a00(zh-TW)</td><td>q</td><td>\u6b32\u53d6\u5f97\u62fc\u97f3\u7684\u6587\u5b57</td></tr><tr><td colspan=\"2\">0:0:0.000000,0:0:5.760000</td><td/><td/></tr><tr><td colspan=\"2\">\u8a71\u8aaa\u5c71\u6771\u767b\u5dde\u5e9c\u6771\u9580\u5916\u6709\u4e00\u5ea7\u5927\u5c71\uff0c\u540d\u53eb\u84ec\u840a\u5c71\u3002</td><td/><td/></tr><tr><td colspan=\"2\">0:0:5.760000,0:0:9.144000</td><td/><td/></tr><tr><td colspan=\"2\">\u5c71\u4e0a\u6709\u500b\u95a3\u5b50\uff0c\u540d\u53eb\u84ec\u840a\u95a3\u3002</td><td/><td/></tr><tr><td colspan=\"2\">0:0:9.144000,0:0:13.968000</td><td/><td/></tr><tr><td colspan=\"2\">\u9019\u95a3\u9020\u5f97\u756b\u68df\u98db\u96f2\uff0c\u73e0\u7c3e\u6372\u96e8\uff0c\u5341\u5206\u58ef\u9e97\u3002</td><td/><td/></tr><tr><td colspan=\"2\">0:0:0.000000,0:0:5.760000</td><td/><td/></tr><tr><td colspan=\"4\">Hu\u00e0shu\u014d sh\u0101nd\u014dng d\u0113ng zh\u014du f\u01d4 d\u014dngm\u00e9n w\u00e0i y\u01d2u y\u012bzu\u00f2 d\u00e0sh\u0101n, m\u00edng ji\u00e0o p\u00e9ngl\u00e1i</td></tr><tr><td>sh\u0101n.</td><td/><td/><td/></tr><tr><td colspan=\"2\">0:0:5.760000,0:0:9.144000</td><td/><td/></tr><tr><td colspan=\"3\">Sh\u0101nsh\u00e0ng y\u01d2u g\u00e8 g\u00e9 zi, m\u00edng ji\u00e0o p\u00e9ngl\u00e1i g\u00e9.</td><td/></tr></table>",
"text": "Timed-text \u6a94\u6848\u7bc4\u4f8b \u82e5\u8f38\u5165\u6587\u672c\u4e0d\u70ba\u82f1\u6587\u6642\uff0c\u9700\u8981\u5f9e Google Translate \u53d6\u5f97\u5176\u539f\u6587\u7684\u7f85\u99ac\u62fc\u97f3\uff0c\u4e26\u4e14\u5c07 \u6b64\u7f85\u99ac\u62fc\u97f3\u53d6\u4ee3\u539f\u6587\uff0c\u5c07\u53e5\u5c64\u7d1a\u7684\u539f\u6587\u6587\u5b57\u6a94\u8f49\u6210\u53e5\u5c64\u7d1a\u7684\u7f85\u99ac\u62fc\u97f3\u6a94\uff0c\u800c\u82e5\u5229\u7528 Google Translate \u53d6\u5f97\u62fc\u97f3\uff0c\u5176\u4e5f\u6703\u5e6b\u6211\u5011\u505a\u65b7\u8a5e\u7684\u52d5\u4f5c\u3002 \u5229\u7528\u540c Communicate to Google \u7684\u65b9\u6cd5\uff0c\u53ea\u8981\u5c07 URL \u6539\u6210\uff0c http://translate.google.com.tw/translate_a/single \u5716\u4e03\u3001\u975e\u82f1\u6587\u6587\u5b57 sbv \u6a94\u8f49\u7f85\u99ac\u62fc\u97f3 sbv \u6a94 4. Audio Converter \u672c\u7bc0\u65e8\u5728\u8aaa\u660e\u5982\u4f55\u5c07 Google Translate TTS \u6240\u5f97\u7684 mp3 \u6a94\u6848\u8f49\u6210 CguAlign \u80fd\u63a5\u53d7\u7684 wav \u6a94\u6848\uff0c\u4f7f\u7528\u81ea\u7531\u8edf\u9ad4\u2500FFmpeg \u4f86\u5e6b\u52a9\u8f49\u6a94\uff0cFFmpeg \u53ef\u4ee5\u57f7\u884c\u97f3\u8a0a\u548c\u8996\u8a0a\u591a\u7a2e \u683c\u5f0f\u7684\u9304\u5f71\u3001\u8f49\u6a94\u3001\u4e32\u6d41\u529f\u80fd\uff0c\u56e0\u9700\u501f\u52a9 FFmpeg \u7684\u5e6b\u52a9\uff0c\u800c FFmpeg \u5c6c\u65bc\u5916\u90e8\u7a0b \u5f0f\uff0c\u5728 Python \u4e2d\u82e5\u9700\u8981\u547c\u53eb\u5916\u90e8\u7684\u7a0b\u5f0f\uff0c\u9700\u8981 import os \u6a21\u7d44\uff0c\u4e26\u4e14\u4f7f\u7528 os.system()"
},
"TABREF4": {
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td>1</td><td>var audioFileName=\"YourAudioFile.wav\" \u5716\u5341\u4e94\u3001\u7dda\u4e0a\u5373\u6642\u7ffb\u8b6f\u7bc4\u4f8b</td></tr><tr><td>2 3</td><td>document.write(\"<div><audio id='mainAudio' src='' controls=controls /></div>\"); document.getElementById(\"mainAudio\").src=audioFileName; \u56db\u3001\u7d50\u8ad6</td></tr><tr><td>4 5</td><td>var playrate= mainAudio.playbackRate document.getElementById(\"playrate\").innerHTML =playrate; \u672c\u7814\u7a76\u7684\u76ee\u7684\u662f\u5229\u7528\u7d14\u6587\u5b57\u6a94\u8f49\u6210\u8a9e\u97f3\u6a94\u7684\u6280\u8853(Text-to-speech)\u7d50\u5408\u8a9e\u97f3\u8fa8\u8b58</td></tr><tr><td>6</td><td>var time= mainAudio.currentTime; (Speech-recognition)\u4e2d\u7684\u97f3\u6587\u5c0d\u9f4a\u6280\u8853(Speech-text Synchronization)\u88fd\u4f5c\u80fd\u5920\u4ee5\u96fb\u8166\u8f14</td></tr><tr><td>7</td><td>document.getElementById(\"audiotime\").innerHTML =time; \u52a9\u8a9e\u8a00\u5b78\u7fd2(Computer-assisted Language Learning)\u70ba\u76ee\u6a19\uff0c\u5e6b\u52a9\u8a9e\u8a00\u5b78\u7fd2\u8005\u80fd\u5920\u501f\u52a9\u6b64</td></tr><tr><td/><td>\u5716\u5341\u56db\u3001\u8b80\u53d6\u97f3\u8a0a\u7684\u7bc4\u4f8b\u7a0b\u5f0f\u78bc \u7cfb\u7d71\u8f03\u8f15\u9b06\u5730\u5be6\u73fe\u8ddf\u8ff0\u5b78\u7fd2\u6cd5(Shadowing technique)\u7684\u4e00\u500b\u7cfb\u7d71\u3002</td></tr><tr><td/><td>\u5728\u5716\u5341\u56db\u7684\u7bc4\u4f8b\u4e2d\uff0c\u5728\u7b2c 2 \u884c\u5148\u5275\u7acb\u4e00\u500b audio \u7269\u4ef6\uff0c\u4e26\u4e14\u5728\u7b2c 3 \u884c\u6307\u5b9a\u6b64\u7269\u4ef6\u6240 \u5728\u6b64\u7cfb\u7d71\u4e2d\uff0c\u4f7f\u7528\u8005\u80fd\u5920\u81ea\u7531\u5730\u53d6\u5f97\u4efb\u4f55\u60f3\u8ddf\u8ff0\u7684\u7d20\u6750\u7684\u6587\u672c\uff0c\u4ee5\u6b64\u6587\u672c\uff0c\u501f\u52a9\u672c\u7cfb\u7d71\uff0c</td></tr><tr><td/><td>\u8981\u8b80\u53d6\u7684\u97f3\u8a0a\u6a94\u6848\uff0c\u7b2c 5 \u884c\u3001\u7b2c 7 \u884c\u80fd\u5920\u5f97\u5230\u6b64\u97f3\u8a0a\u7684\u64ad\u653e\u901f\u5ea6\u548c\u76ee\u524d\u6240\u64a5\u653e\u7684\u97f3 \u80fd\u5920\u5f9e Google Translate \u53d6\u5f97\u6b64\u6587\u672c\u7684 TTS \u8a9e\u97f3\u6a94\u53ca\u5176\u5df2\u5c0d\u9f4a\u7684\u5e36\u6709\u6642\u9593\u9ede\u7684\u6587\u672c</td></tr><tr><td/><td>\u8a0a\u6642\u9593\u9ede\uff0c\u6b64\u97f3\u8a0a\u6642\u9593\u9ede\u662f\u7528\u4f86\u505a\u97f3\u6587\u540c\u6b65\u975e\u5e38\u91cd\u8981\u7684\u8cc7\u8a0a\u3002 (Timed-text)\uff0c\u4e0d\u540c\u65bc\u4ee5\u5f80\u8f03\u5e38\u898b\u7684\u53e5\u5c64\u7d1a(Sentence-level)\u7684\u6587\u672c\uff0c\u672c\u7cfb\u7d71\u904b\u7528\u8a9e\u97f3\u8fa8\u8b58</td></tr><tr><td/><td>\u6280\u8853\u80fd\u5920\u88fd\u4f5c\u51fa\u8a5e\u5c64\u7d1a(Word-level)\u7684\u6587\u672c\uff0c\u4ee5\u6b64\u5e36\u6709\u6642\u9593\u9ede\u7684\u6587\u672c\u85c9\u7531\u6211\u5011\u7684\u7db2\u7ad9\u700f</td></tr><tr><td/><td>\u4ee5\u985e\u540c\u524d\u8ff0\u53d6\u5f97\u4e2d\u6587\u62fc\u97f3\u7684\u65b9\u6cd5\uff0c\u4e0d\u50c5\u80fd\u5920\u53d6\u5f97\u55ae\u5b57\u7684\u62fc\u97f3\uff0c\u540c\u6a23\u4e5f\u80fd\u5920\u53d6\u5f97\u55ae\u5b57 \u89bd\uff0c\u5373\u662f\u4e00\u672c\u97f3\u6587\u540c\u6b65\u7684\u96fb\u5b50\u6709\u8072\u66f8\uff0c\u5728\u6b64\u7db2\u7ad9\u4e0a\uff0c\u4e0d\u50c5\u53ef\u4ee5\u9032\u884c\u8ddf\u8ff0\u5b78\u7fd2\u6cd5\u5b78\u7fd2\uff0c\u4e5f</td></tr><tr><td/><td>\u7684\u7ffb\u8b6f\uff0c\u6211\u5011\u53ef\u4ee5\u7528\u6b64\u529f\u80fd\u4f86\u5be6\u4f5c\u7dda\u4e0a\u67e5\u8a62\u5b57\u5178\u7684\u529f\u80fd\uff0c\u8207\u62fc\u97f3\u53d6\u5f97\u7684\u65b9\u6cd5\u4e0d\u540c\u7684 \u53ef\u4ee5\u505a\u4e00\u500b\u7dda\u4e0a\u67e5\u8a62\u5b57\u5178\u7684\u529f\u80fd\uff0c\u5176\u4e0d\u50c5\u53ef\u4ee5\u63d0\u4f9b\u97f3\u6587\u540c\u6b65\u7684\u96fb\u5b50\u66f8\u4f9b\u4f7f\u7528\u8005\u95b1\u8b80\u6587\u7ae0\uff0c</td></tr><tr><td/><td>\u662f\uff0c\u7ffb\u8b6f\u529f\u80fd\u5fc5\u9808\u6307\u5b9a\u597d sl \u548c tl \u5169\u500b\u53c3\u6578\uff0c\u5176\u610f\u7fa9\u4ee3\u8868 source language \u4f86\u6e90\u8a9e\u8a00 \u4e5f\u53ef\u4ee5\u8b93\u4f7f\u7528\u8005\u85c9\u7531\u6717\u8aa6\u6587\u7ae0\u7684\u65b9\u5f0f\uff0c\u4e26\u900f\u904e\u8ddf\u8ff0\u5b78\u7fd2\u6cd5\u7684\u5be6\u4f5c\u548c\u5373\u6642\u7ffb\u8b6f\u7684\u6548\u679c\uff0c\u4ee5</td></tr><tr><td/><td>\u548c target language \u76ee\u6a19\u8a9e\u8a00\u3002 \u9054\u5230\u81ea\u6211\u5167\u5316\u5b78\u7fd2\u53ca\u589e\u9032\u8a9e\u8a00\u80fd\u529b\u3002</td></tr></table>",
"text": "\u5716\u5341\u4e09\u3001\u5c07 lrc \u6a94\u8f49\u6210\u7db2\u9801\u4e0a\u7684\u6a19\u7c64\u8cc7\u8a0a \u4f7f\u7528 HTML5 \u65b0\u589e\u7684 audio \u6a19\u7c64\uff0c\u80fd\u5920\u5148\u5275\u5efa\u4e00\u500b\u64ad\u653e\u97f3\u8a0a\u7684\u7269\u4ef6\uff0c\u8ce6\u4e88\u6b64\u7269\u4ef6\u4e00 \u500b\u7368\u7279\u7684 ID\u2500mainAudio\uff0c\u518d\u5229\u7528 HTML DOM \u7684\u7269\u4ef6\uff0c\u80fd\u5920\u6307\u5b9a mainAudio \u8981\u8b80 \u53d6\u7684\u97f3\u8a0a\u6a94\u6848\u4ee5\u53ca\u6b64\u97f3\u8a0a\u7684\u4e00\u4e9b\u8cc7\u8a0a\u3002"
}
}
}
} |