File size: 71,141 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 |
{
"paper_id": "O06-1010",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:07:01.579798Z"
},
"title": "",
"authors": [],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "",
"pdf_parse": {
"paper_id": "O06-1010",
"_pdf_hash": "",
"abstract": [],
"body_text": [
{
"text": "\u6cd5(maximum likelihood, ML)\uff0c\u6c7a\u5b9a\u6700\u4f73\u7684\u591a\u8a9e\u8fa8\uf9fc\u5e8f\uf99c\u3002\u4f5c\u6cd5\u4e0a\uf9d0\u4f3c\u5c0d\u8a9e\u97f3\u8fa8\uf9fc\u5e8f\uf99c\u505a\u9a57\u8b49(verification) \u4e4b\u8655\uf9e4 [3] \uff1b\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u7684\u8868\u73fe\u53d6\u6c7a\u65bc\u5f8c\u7aef\u6700\u4f73\u5e8f\uf99c\u9078\u64c7\u4e4b\u6548\u679c\u3002\uf9dd\u7528\u9078\u64c7\u6700\u5927\u4f3c\u7136\u7684\u65b9\u6cd5\u7f3a\u9ede\u5728\u65bc\uff0c \u591a\u8a9e\u8fa8\uf9fc\u7684\u6548\u679c\u6703\u53d7\u5230 ML \u65b9\u6cd5\u7684\u9650\u5236\uff0c\u4e14\u8fa8\uf9fc\u7684\u591a\u8a9e\uf906\u578b\u9700\u8981\u53e6\u5916\u8003\u616e\uff0c\ufa00\u5272\u51fa\u8a9e\uf906\u5167\uf967\u540c\u8a9e\u8a00\u7684\u6bb5\uf918\u3002 \u7b2c\u4e09\uf9d0\u4f5c\u6cd5\uff1a\u85c9\u7531\u5b9a\u7fa9\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u55ae\u5143\u96c6 [4] \uff0c\u5408\u4f75\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20\u6a21\u578b\uff0c\uf92d\u9032\ufa08\u591a\u8a9e\u8a9e\u97f3\u8fa8 \uf9fc\u3002\u672c\uf941\u6587\u4e43\u57fa\u65bc\u6b64\u65b9\u6cd5\uff0c\u63a2\u8a0e\u5982\u4f55\u5b9a\u7fa9\u51fa\u6709\u6548\u7684\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u55ae\u5143\u6a21\u578b\u3002 \u5728\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\u4e4b\u5efa\uf9f7\u53ef\u4ee5\u6b78\u7d0d\u70ba\u4e09\u7a2e\u65b9\u5f0f\u3002\u9996\u5148\uff0c\u6211\u5011\u53ef\u4ee5\u76f4\u63a5\u5408\u4f75\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20\u96c6\uff0c\u5efa\uf9f7 \u591a\u8a9e\u97f3\u7d20\u6a21\u578b\uff0c\u4f46\u662f\u9019\u7a2e\u65b9\u6cd5\u6c92\u6709\u8003\u616e\u591a\u8a9e\u97f3\u7d20\u9593\uf96b\uf969\u5206\u4eab\u7684\u7279\u6027\u3002\u7b2c\u4e8c\uff0c\u85c9\u7531\u5c0d\u7167\u570b\u969b\u97f3\u7d20\u6a19\u6e96\u5b9a\u7fa9\uff0c \u8003\u616e\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20\uff0c\u9054\u5230\u591a\u8a9e\u97f3\u7d20\u9593\uf96b\uf969\u5171\u7528\u7684\u7279\u6027\uff0c\u4f46\u662f\u6b64\u4f5c\u6cd5\u4e0a\u7f3a\u4e4f\u8cc7\uf9be\u7d71\u8a08\u7684\u5206\u6790\uff0c\u800c\u662f\u7531 \u5c08\u5bb6\u77e5\uf9fc\u6c7a\u5b9a\u5404\u97f3\u7d20\u5b9a\u7fa9\u3002\u570b\u969b\u97f3\u7d20\u6a19\u6e96\u5b9a\u7fa9\u5305\u542b\u6709\uff1aInternational Phonetic Alphabet (IPA) [5] \u3001Speech Assessment Methods Phonetic Alphabet (SAMPA) [6] \u548c Worldbet [7] \u7b49\u3002\u7b2c\u4e09\uff0c\u4f30\u8a08\u591a\u8a9e\u8a9e\u97f3\u97f3\u7d20\u9593\u76f8\u4f3c\u7a0b \ufa01\uff0c\u7531\u4e0b\u800c\u4e0a\u968e\u5c64\u5f0f\u9032\ufa08\u591a\u8a9e\u97f3\u7d20\u5408\u4f75\uff0c\u4ee5\u5b9a\u7fa9\u591a\u8a9e\u97f3\u7d20\u96c6\u3002\u591a\u8a9e\u8a9e\u97f3\u97f3\u7d20\u9593\u76f8\u4f3c\ufa01\u7684\uf97e\u6e2c\uff0c\u53ef\u4ee5\uf9dd\u7528 Bhattacharyya distance [8] \u6216\u8005\u662f Kullback-Leibler (KL) divergence [9] ",
"cite_spans": [
{
"start": 72,
"end": 75,
"text": "[3]",
"ref_id": "BIBREF2"
},
{
"start": 188,
"end": 191,
"text": "[4]",
"ref_id": "BIBREF3"
},
{
"start": 451,
"end": 454,
"text": "[5]",
"ref_id": "BIBREF4"
},
{
"start": 508,
"end": 511,
"text": "[6]",
"ref_id": "BIBREF5"
},
{
"start": 523,
"end": 526,
"text": "[7]",
"ref_id": "BIBREF6"
},
{
"start": 614,
"end": 617,
"text": "[8]",
"ref_id": "BIBREF7"
},
{
"start": 655,
"end": 658,
"text": "[9]",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "D 1 2 1 1 2 1 2 1 2 1 2 1 2 ( ) ( ) l n 8 2 2 T bha D \u03bc \u03bc \u03bc \u03bc \u2212 1 \u2211 + \u2211 \u2211 + \u2211 \u23a1 \u23a4 = \u2212 \u2212 + \u23a2 \u23a5 \u23a3 \u23a6 \u2211 \u2211 (\u5f0f 1) \u5176\u4e2d\uff0c \u03bc \u548c \u2211 \u5206\u5225\u8868\u793a\u97f3\u7d20\u6a21\u578b\u7684\u5e73\u5747\u503c\u548c\u8b8a\uf962\uf969\u5411\uf97e\uff0cT \u662f\u8f49\u7f6e\u77e9\u9663\u3002\u53e6\u5916\uff0c\u53ef\u4ee5\uf9dd\u7528 Kullback-Leibler (KL) divergence [9]\uf92d\u6c7a\u5b9a\uf978\u500b\u6a5f\uf961\u5206\u4f48\u7684\u76f8\u4f3c\ufa01 KL D \u3002\u4ee5 KL-divergence \u4f30\u7b97\uf978\u500b\u9ad8\u65af\u5206\u4f48 1 1 ( , ) N \u03bc \u2211 \u548c 2 2 ( , ) N \u03bc \u2211 \u7684\u76f8\u4f3c\ufa01\uff0c\u8868\u793a\u5982\u4e0b\uff1a ( ) ( ) ( ) 1 1 1 1 2 1 2 1 1 2 2 | | 1 ln tr 2 | | T KL D d \u03bc \u03bc \u03bc \u03bc \u2212 \u2212 \u239b \u239e \u2211 = + \u2211 \u2211 + \u2212 \u2211 \u2212 \u239c \u239f \u2211 \u239d \u23a0 \u2212 (",
"eq_num": "\u5f0f 2"
}
],
"section": "",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "( | ) i l k P x \u03c9 \uff0c\u5176\u4e2d i l x \u8868\u793a\u7b2c l \u500b\u97f3\u7d20\u4e2d\u4e4b\u7b2c \u500b\u8a13\uf996\u8cc7\uf9be\u8a08\u7b97\u97f3\u7d20\u4e4b\u9593\u53d6\u5c0d\uf969\u7528\u8ddd\uf9ea\u7684\u65b9\u5f0f\u5448\u73fe\u97f3\u7d20\u9593\u5f7c\u6b64 \u7684\u95dc\u4fc2 \uff0c\u4ee5\u5efa\uf9f7\u8072\u5b78\u76f8\u4f3c\ufa01\u77e9\u9663 i log( ( | )) i l k P x \u03c9 ( ) kl N N a \u00d7 = A \u3002\u70ba\u5efa\uf9f7\u4e00\u500b\u5c0d\u7a31\u8072\u5b78\u76f8\u4f3c\ufa01\u77e9\u9663\uff0c\u6211\u5011\u5c0d\u5176 \u8a08\u7b97\u5c0d\u89d2\u5e73\u5747\u503c\u3002 1 1 1 1 log( ( | )) log( ( | )) 2 I J i j l k k l i j kl P x P x I J a \u03c9 \u03c9 = = + = \u2211 \u2211 (",
"eq_num": "\u5f0f 3"
}
],
"section": "",
"sec_num": null
},
{
"text": "( , 1 2 1 2 ( , ) , , . . . , , , , . . . , l l l k k k l k l k N N h v v w w w w w w = = ) (\u5f0f 4) \u8003\u616e\u97f3\u7d20\u767c\u8072\u53d7\u5230\u76f8\u9130\u97f3\u7d20\u7684\u5f71\u97ff\uff0c\u4ee5\u4e09\uf99a\u97f3\u7d20 \u70ba\u4e2d\u5fc3\u4e4b\u7a7a\u9593\u76f8\u4f3c\ufa01\u53ef\u4ee5\uf9dd\u7528\u8207\u53f3\u908a\u6587\u8108\u76f8\u95dc\u4e4b\u5411\uf97e \u53ca\u8207\u5de6\u908a\u6587\u8108\u76f8\u95dc\u4e4b\u5411\uf97e \u4e4b\u63cf\u8ff0\uff1b \u548c \u5206\u5225\u8868\u793a\uf9dd\u7528\u89c0\u6e2c\u8996\u7a97\u65bc HAL \u7a7a\u9593\u5167\u7d71\u8a08\u4e4b\u97f3\u7d20\u76f8\u95dc\u6b0a \u91cd\uff0c l \u548c k \u5206\u5225\u8868\u793a\ufa08\u8207\uf99c\u4e4b\uf96a\u5f15\u3002 , l k h l v k v l N w k N w \u5728 HAL \u7a7a\u9593\u4e2d\uff0c\u6b0a\u91cd\u4e4b\u8a08\u7b97\u9700\u8003\u616e\u6b63\u898f\u5316(normalization)\u56e0\u7d20\uff0c\u672c\uf941\u6587\uf9dd\u7528\u5728\u8cc7\u8a0a\u6aa2\uf96a\u4e2d\u76f8\u7576\u91cd\u8981\u4e4b \uf96b\uf969 tf",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "(term frequency and inverse document frequency) [13] \u5176\u4e2d\uff0c \u03b1 \u662f\u4e00\u500b\u6b0a\u91cd\u56e0\u5b50\uff0c\u8ca0\u8cac\u878d\u5408\u8072\u5b78\u76f8\u4f3c\ufa01\u548c\u524d\u5f8c\u6587\u8108\u7684\u95dc\uf997\u3002\u91dd\u5c0d\u76f8\u4f3c\ufa01\u77e9\u9663 \u548c \uff0c\uf941\u6587\u4e2d\u5c0d ",
"cite_spans": [
{
"start": 48,
"end": 52,
"text": "[13]",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\uff0c\u91cd\u65b0\u4f30\u8a08\u6bcf\u500b\u5411\uf97e\u7dad\ufa01\u4e4b\u6b0a\u91cd\uff0c\u8868\u793a\u5982\u4e0b\uff1a idf \u00d7 log i i i N w w C = \u00d7 (\u5f0f 5) \u5176\u4e2d\uff0c \u6307\u5728\u5411\uf97e \u6216\u5411\uf97e \u4e2d\u7b2c i \u500b\u7dad\ufa01\u4e4b\u6b0a\u91cd\uff1b \u6307\u5728\u6240\u6709\u5411\uf97e\u4e2d\uff0c\u7b2c i \u500b\u7dad\ufa01\u4e4b\u6b0a\u91cd\uf967\u70ba\uf9b2\u7684\u5411\uf97e \u500b\uf969\uff1b \u70ba\u5411\uf97e\u7e3d\u500b\uf969\u6216\u8fa8\uf9fc\u55ae\u5143\u500b\uf969\u3002 i w l v k v i C N 3.3. \u4e09\uf99a\u97f3\u7d20\u5411\uf97e\uf97e\u5316\u7fa4\u805a\u8003\u616e\u76f8\u4f3c\ufa01\u77e9\u9663\u8cc7\uf9be\u878d\u5408 \u7d93\u904e\u524d\uf978\u5c0f\u7bc0\u5206\u6790\uff0c\u4e09\uf99a\u97f3\u7d20\u53ef\u4ee5\u5728\u8072\u5b78\u7a7a\u9593\u548c\u8a9e\u8a00\u8d85\u7a7a\u9593\u4e2d\uff0c\u7528\u5411\uf97e\u7684\u65b9\u5f0f\u8868\u793a\u5728\u7a7a\u9593\u4e2d\u7684\u76f8\u4f3c\u7a0b \ufa01\u3002\u672c\uf941\u6587\u5206\u6790\u8072\u5b78\u76f8\u4f3c\ufa01\u77e9\u9663 ( ) kl N N a \u00d7 = A \u548c\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u77e9\u9663 ( ) kl N N h \u00d7 = H \uff0c\u540c\u6642\u8003\u616e\u8072\u5b78\u76f8\u4f3c\ufa01 \u548c\u524d\u5f8c\u6587\u8108\u767c\u97f3\u7684\u7279\u6027\uff0c\u57fa\u65bc\u524d\u5f8c\u6587\u8108\u76f8\u95dc\u4e4b\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\uff0c\u5408\u4f75\u76f8\u4f3c\u4e4b\u767c\u97f3\u627e\u51fa\u6700\u70ba\ufa1d\u7c21\u6709\u6548\u7684\u591a\u8a9e\u97f3 \u7d20\u6a21\u578b\u5b9a\u7fa9\u3002\u4f5c\u6cd5\u4e0a\uff0c\uf96b\u8003\u8cc7\uf9be\u878d\u5408\u7684\u65b9\u6cd5[14]\uff0c\u672c\uf941\u6587\uf9dd\u7528\u52a0\u6cd5\u878d\u5408\u7684\u6280\u8853(sum rule)\uff0c\u7d50\u5408\uf978\u76f8\u4f3c\ufa01\u77e9 \u9663 \u548c H \uff0c\u5c07\u8072\u5b78\u76f8\u4f3c\ufa01\u548c\u524d\u5f8c\u6587\u8108\u7684\u97f3\u7d20\u7279\u5fb5\u4f5c\u6574\u5408\uff0c\u8868\u793a\u5982\u4e0b\uff1a A ,",
"eq_num": "(1 )"
}
],
"section": "",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "(",
"eq_num": "(1 )"
}
],
"section": "",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u5176\uf969\u503c\u6b63\u898f\u5283\uff0c\u5c07\u8072\u5b78\u548c\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u77e9\u9663\u7684\u5206\uf969\u7d50\u5408\uff0c\u7a31\u77e5\uf9fc\u878d\u5408\u76f8\u4f3c\ufa01\u77e9\u9663 \u70ba\u4e00\u500b\u5c0d \u7a31\u77e9\u9663\uff0c\ufa08 l \u8207\uf99c \u5747\u8868\u793a\u67d0\u4e00\u97f3\u7d20\u8207\u5176\u4ed6\u97f3\u7d20\u76f8\u4f3c\ufa01\u4e4b\u5411\uf97e\u3002\u70ba\uf9ba\u5efa\uf9f7\u6709\u6548\ufa1d\u7c21\u7684\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\u65bc\u591a\u8a9e \u8a9e \u97f3 \u8fa8 \uf9fc \u4e4b \u61c9 \u7528 \uff0c \u672c \uf941 \u6587 \uf9dd \u7528 \u5411 \uf97e \uf97e \u5316 (vector quantization, VQ) \u7684 \u65b9 \u6cd5 [12] \uff0c \u5f9e \u8cc7 \uf9be \u5206 \u6790 \u7684 \u89d2 \ufa01 (data-driven)\uff0c\u5c07\u539f\u672c\u4e09\uf99a\u97f3\u7d20\u81ea\u52d5\u5730\u4f9d\u64da\u97f3\u7d20\u76f8\u4f3c\ufa01\u5206\u6790\uff0c\u5408\u4f75\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9\u3002\u5411\uf97e\uf97e\u5316\u70ba\u662f\u4e00\u7a2e\u975e\u76e3\u7763 \u5f0f\u7684\u7fa4\u96c6\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5c07\u5206\u6563\u7684\u8cc7\uf9be\u7fa4\u96c6\u6210\u6709\u610f\u7fa9\u7684\uf9d0\u5225\u3002\u4e09\uf99a\u97f3\u7d20\u5728\u76f8\u4f3c\ufa01\u77e9\u9663\u5206\u6790\u5f8c\uff0c\u53ef\u7528\u5411\uf97e\u65b9 \u5f0f\u8868\u793a\u5176\u7a7a\u9593\u5ea7\u6a19\uff0c\uf941\u6587\u5f15\u7528[15]\u5728\u77e9\u9663\u4e2d\uff0c\uf978\u5411\uf97e\u593e\u89d2\u7684\u8a08\u7b97\u65b9\u6cd5\uff0c\u56e0\u6b64\uf978\u97f3\u7d20\u7684\u76f8\u4f3c\ufa01\u8a08\u7b97\u70ba \uff0c \u8a08\u7b97\u5982\u4e0b\uff1a A H ( ) kl N N s \u00d7 = S k ( , ) l k c s s 1 2 2 1 1 ( , ) N l k i i l k i l k N N l k l k l k s s s s c s s s s s s = = = \u00d7 \u2022 = = \u22c5 \u00d7 \u2211 \u2211 \u2211 (",
"eq_num": "\u5f0f 7"
}
],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Automatic Segmentation and Identification of Mixed-Language Speech Using Delta-BIC and LSA-Based GMMs",
"authors": [
{
"first": "Chung-Hsien",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Yu-Hsien",
"middle": [],
"last": "Chiu",
"suffix": ""
},
{
"first": "Chi-Jiun",
"middle": [],
"last": "Shia",
"suffix": ""
},
{
"first": "Chun-Yu",
"middle": [],
"last": "Lin",
"suffix": ""
}
],
"year": 2006,
"venue": "IEEE Transactions on audio, speech, and language processing",
"volume": "14",
"issue": "1",
"pages": "266--276",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chung-Hsien Wu, Yu-Hsien Chiu, Chi-Jiun Shia, and Chun-Yu Lin, 2006. Automatic Segmentation and Identification of Mixed-Language Speech Using Delta-BIC and LSA-Based GMMs. IEEE Transactions on audio, speech, and language processing, vol. 14, no. 1, pp. 266-276.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Verbmobil: Foundations of Speech-to-Speech Translation",
"authors": [
{
"first": "Alex",
"middle": [],
"last": "Waibel",
"suffix": ""
},
{
"first": "Hagen",
"middle": [],
"last": "Soltau",
"suffix": ""
},
{
"first": "Tanja",
"middle": [],
"last": "Schultz",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Schaaf",
"suffix": ""
},
{
"first": "Florian",
"middle": [],
"last": "Metze",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alex Waibel, Hagen Soltau, Tanja Schultz, Thomas Schaaf, and Florian Metze, 2000. Multilingual Speech Recognition. Chapter in Verbmobil: Foundations of Speech-to-Speech Translation, Springer-Verlag.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Vocabulary Independent Discriminative Utterance Verification for Nonkeyword Rejection in Subword based Speech Recognition",
"authors": [
{
"first": "A",
"middle": [],
"last": "Rafid",
"suffix": ""
},
{
"first": "Chin-Hui",
"middle": [],
"last": "Sukkar",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 1996,
"venue": "IEEE Transactions on Speech and Audio Processing",
"volume": "4",
"issue": "6",
"pages": "420--429",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rafid A. Sukkar and Chin-Hui Lee, 1996. Vocabulary Independent Discriminative Utterance Verification for Nonkeyword Rejection in Subword based Speech Recognition. IEEE Transactions on Speech and Audio Processing, vol. 4, no. 6, pp. 420-429.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Generation of robust phonetic set and decision tree for Mandarin using chi-square testing",
"authors": [
{
"first": "Yeou-Jiunn",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Chung-Hsien",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Yu-Hsien",
"middle": [],
"last": "Chiu",
"suffix": ""
},
{
"first": "Hsiang-Chuan",
"middle": [],
"last": "Liao",
"suffix": ""
}
],
"year": 2002,
"venue": "Speech Communication",
"volume": "38",
"issue": "",
"pages": "349--364",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yeou-Jiunn Chen, Chung-Hsien Wu, Yu-Hsien Chiu, and Hsiang-Chuan Liao, 2002. Generation of robust phonetic set and decision tree for Mandarin using chi-square testing. Speech Communication, vol. 38(3-4), pp. 349-364.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Mathews' Chinese-English Dictionary, Caves",
"authors": [
{
"first": "R",
"middle": [
"H"
],
"last": "Mathews",
"suffix": ""
}
],
"year": 1975,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mathews, R. H., 1975. Mathews' Chinese-English Dictionary, Caves, 13th printing.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Computer-Coded Phonemic Notation of Individual Languages of the European Community",
"authors": [
{
"first": "J",
"middle": [
"C"
],
"last": "Wells",
"suffix": ""
}
],
"year": 1989,
"venue": "J. IPA",
"volume": "19",
"issue": "",
"pages": "32--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. C. Wells, 1989. Computer-Coded Phonemic Notation of Individual Languages of the European Community. J. IPA, 19, pp. 32-54.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "ASCII Phonetic Symbols for the World's Languages: Worldbet",
"authors": [
{
"first": "James",
"middle": [
"L"
],
"last": "Hieronymus",
"suffix": ""
}
],
"year": 1993,
"venue": "Journal of the International Phonetic Association",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "James L. Hieronymus, 1993. ASCII Phonetic Symbols for the World's Languages: Worldbet. Journal of the International Phonetic Association.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Phone clustering using the Bhattacharyya distance",
"authors": [
{
"first": "Brian",
"middle": [],
"last": "Mak",
"suffix": ""
},
{
"first": "Etienne",
"middle": [],
"last": "Barnard",
"suffix": ""
}
],
"year": 1996,
"venue": "Proc. ICSLP",
"volume": "",
"issue": "",
"pages": "2005--2008",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Brian Mak and Etienne Barnard, 1996. Phone clustering using the Bhattacharyya distance. in Proc. ICSLP, pp. 2005-2008.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "A Distance Measure Between GMMs Based on the Unsented Transform and its Application to Speaker Recognition",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Goldberger",
"suffix": ""
},
{
"first": "Hagai",
"middle": [],
"last": "Aronowitz",
"suffix": ""
}
],
"year": 2005,
"venue": "Proc. of EUROSPEECH 2005",
"volume": "",
"issue": "",
"pages": "1985--1988",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jacob Goldberger and Hagai Aronowitz, 2005. A Distance Measure Between GMMs Based on the Unsented Transform and its Application to Speaker Recognition. in Proc. of EUROSPEECH 2005, pp. 1985-1988, Lisbon, Portugal.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "The Sound Pattern of English",
"authors": [
{
"first": "N",
"middle": [],
"last": "Chomsky",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Halle",
"suffix": ""
}
],
"year": 1968,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chomsky, N. and Halle, M., 1968. The Sound Pattern of English. New York: Harper & Row.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Modelling parsing constraints with high-dimensional context space",
"authors": [
{
"first": "C",
"middle": [],
"last": "Burgess",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Lund",
"suffix": ""
}
],
"year": 1997,
"venue": "Language and Cognitive Processes",
"volume": "12",
"issue": "",
"pages": "177--210",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Burgess, C. and Lund, K., 1997. Modelling parsing constraints with high-dimensional context space. Language and Cognitive Processes, 12:177-210.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Quantization",
"authors": [
{
"first": "M",
"middle": [],
"last": "Robert",
"suffix": ""
},
{
"first": "David",
"middle": [
"L"
],
"last": "Gray",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Neuhoff",
"suffix": ""
}
],
"year": 1998,
"venue": "IEEE Transactions on Information Theory",
"volume": "44",
"issue": "6",
"pages": "2325--2383",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robert M. Gray and David L. Neuhoff, 1998. Quantization. IEEE Transactions on Information Theory, vol. 44, no. 6, pp. 2325-2383.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Term-weighting Approaches in Automatic Text Retrieval",
"authors": [
{
"first": "G",
"middle": [],
"last": "Salton",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Buckley",
"suffix": ""
}
],
"year": 1988,
"venue": "Information Processing Management",
"volume": "24",
"issue": "5",
"pages": "513--523",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Salton and C. Buckley, 1988. Term-weighting Approaches in Automatic Text Retrieval. Information Processing Management, vol. 24, no. 5, pp. 513-523.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "On Combining Classifiers",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Kittler",
"suffix": ""
},
{
"first": "Mohamad",
"middle": [],
"last": "Hatef",
"suffix": ""
},
{
"first": "P",
"middle": [
"W"
],
"last": "Robert",
"suffix": ""
},
{
"first": "Jiri",
"middle": [],
"last": "Duin",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Matason",
"suffix": ""
}
],
"year": 1998,
"venue": "IEEE Transactions on Pattern Analysis and Machine Intelligence",
"volume": "20",
"issue": "3",
"pages": "226--239",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Josef Kittler, Mohamad Hatef, Robert P.W. Duin, and Jiri MatasOn, 1998. On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Exploiting latent semantic information in statistical language modeling",
"authors": [
{
"first": "Jerome",
"middle": [
"R"
],
"last": "Bellegarda",
"suffix": ""
}
],
"year": 2000,
"venue": "Proc. IEEE",
"volume": "88",
"issue": "",
"pages": "1279--1296",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jerome R. Bellegarda, 2000. Exploiting latent semantic information in statistical language modeling. Proc. IEEE, vol. 88, no. 8, pp. 1279-1296.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "A modified K-means clustering algorithm for use in isolated work recognition",
"authors": [
{
"first": "G",
"middle": [],
"last": "Jay",
"suffix": ""
},
{
"first": "Lawrence",
"middle": [
"R"
],
"last": "Wilpon",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Rabiner",
"suffix": ""
}
],
"year": 1985,
"venue": "IEEE Transactions on Acoustics, Speech, and Signal Proc",
"volume": "33",
"issue": "3",
"pages": "587--594",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jay G. Wilpon and Lawrence R. Rabiner, 1985. A modified K-means clustering algorithm for use in isolated work recognition. IEEE Transactions on Acoustics, Speech, and Signal Proc., vol. 33, no. 3, pp. 587-594.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Speech feature smoothing for robust ASR",
"authors": [
{
"first": "Chia-Ping",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jeff",
"middle": [],
"last": "Bilmes",
"suffix": ""
},
{
"first": "P",
"middle": [
"W"
],
"last": "Daniel",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ellis",
"suffix": ""
}
],
"year": 2005,
"venue": "Proc. ICASSP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chia-Ping Chen, Jeff Bilmes and Daniel P. W. Ellis, 2005. Speech feature smoothing for robust ASR. in Proc. ICASSP, Philadelphia PA.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Statistical Methods for Speech Recognition",
"authors": [
{
"first": "D",
"middle": [],
"last": "Johnston",
"suffix": ""
}
],
"year": 1997,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Johnston, D., 1997. Statistical Methods for Speech Recognition. The MIT Press, Cambridge, MA.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Progress in dynamic programming search for LVCSR",
"authors": [
{
"first": "H",
"middle": [],
"last": "Ney",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Ortmanns",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of the IEEE",
"volume": "88",
"issue": "",
"pages": "1224--1240",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Ney and S. Ortmanns, 2000. Progress in dynamic programming search for LVCSR. Proceedings of the IEEE, vol. 88, no. 8, pp. 1224-1240.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"uris": null,
"type_str": "figure",
"num": null,
"text": "\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\u4e4b\u591a\u8a9e\u8fa8\uf9fc\u97f3\u7d20\u6b63\u78ba\uf961 (\u62ec\u5f27\u5167\u8868\u8fa8\uf9fc\u55ae\u5143\u4e4b\u500b\uf969) ====================== English Across Taiwan, EAT ====================== --------------------------------------Triphone Tree-Search Results------------------------------------ACCURACY"
},
"TABREF0": {
"type_str": "table",
"html": null,
"text": "\u7684\u65b9\u6cd5\uff0c\u8a08\u7b97\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\u9593\u7684\u8ddd \uf9ea\uff0c\u6c7a\u5b9a\u76f8\u4f3c\ufa01\u4ee5\u5b9a\u7fa9\u591a\u8a9e\u97f3\u7d20\u96c6\u3002\u6b64\u4f5c\u6cd5\u4e0a\uff0c\u540c\u6642\u8003\u616e\u591a\u8a9e\u97f3\u7d20\u9593\uf96b\uf969\u5206\u4eab\u7684\u7279\u6027\uff0c\u4e26\uf9dd\u7528\u8cc7\uf9be\u7d71\u8a08\u5206 \u6790\u6c7a\u5b9a\u97f3\u7d20\u5b9a\u7fa9\u3002\u4f46\u662f\u7f3a\u9ede\u5728\u65bc\u8a08\u7b97\u6a21\u578b\uf96b\uf969\u9593\u7684\u8ddd\uf9ea\uff0c\u8207\u5be6\u969b\u8fa8\uf9fc\u6f14\u7b97\u6cd5\u5728\u57f7\ufa08\u6642\uff0c\u6240\u8003\u616e\u7684\u8072\u5b78\u76f8\u4f3c \ufa01(acoustic likelihood)\uf967\u7b26\u3002 \u672c\uf941\u6587\u63a2\u8a0e\u4e2d\u82f1\u6587\u4e4b\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u7814\u7a76\uff0c\u5f9e\u4e2d\u82f1\u6587\u57fa\u672c\u97f3\u7d20\u4f5c\u5206\u6790\u3002\u4e2d\u6587\u53ef\u4ee5\u5206\u70ba 37 \u500b\u97f3\u7d20\uff0c\u82f1 \u6587\u53ef\u5206\u70ba 39 \u500b\u97f3\u7d20\u3002\u8003\u616e\u8a9e\u97f3\u767c\u97f3\u5171\u8072\u7684\u73fe\u8c61(co-articulation)\uff0c\u672c\uf941\u6587\u5b9a\u7fa9\u524d\u5f8c\u6587\u76f8\u95dc\u4e4b\u4e09\uf99a\u97f3\u7d20\u6a21\u578b (contextual tri-phone models)\uff0c\u9032\u4e00\u6b65\u5c0d\u8a9e\u97f3\u767c\u97f3\u76f8\u4f3c\ufa01\u4f5c\u8072\u5b78\u76f8\u4f3c\ufa01(acoustic likelihood)\u5206\u6790\u3002\u6b64\u5916\uf901\u5c0e\u5165 \u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u5206\u6790(hyperspace analog to language, HAL)\uff0c\u8003\uf97e\u4e09\uf99a\u97f3\u8fa8\uf9fc\u55ae\u5143\u524d\u5f8c\u6587\u8108\u4e4b\u95dc\u4fc2\uff0c\u4ee5\u6539 \u5584\u904e\u53bb\u55ae\u7d14\u8003\uf97e\u6a21\u578b\uf96b\uf969\u8072\u5b78\u76f8\u4f3c\ufa01\uf92d\uf97e\u6e2c\u8a9e\u97f3\u97f3\u7d20\u9593\u76f8\u4f3c\ufa01\u4e4b\u65b9\u5f0f\uff0c\u4ee5\u6c7a\u5b9a\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\uff0c\u7b26\u5408\u8a9e\u97f3\u767c \u97f3\u4e2d\u53d7\u524d\u5f8c\u6587\u5f71\u97ff\u4e4b\u7279\u6027\u3002\u6700\u5f8c\uff0c\u4ee5\u8cc7\uf9be\u878d\u5408\u7684\u6280\u8853\u5408\u4f75\u5b9a\u7fa9\u767c\u97f3\u76f8\u4f3c\u7684\u97f3\u7d20\u3002\u5be6\u9a57\u8a55\u4f30\uff0c\uf9dd\u7528\u81ea\ufa08\u958b\u767c \u7684\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u7cfb\u7d71\uff0c\u4f7f\u7528\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(hidden Markov model, HMM)\uff0c\u5efa\uf9f7\u4ee5\u97f3\u7d20\u70ba\u57fa\u790e\u7684\u8072\u5b78\u6a21",
"content": "<table><tr><td colspan=\"3\">\u5b9a\u7fa9\u548c 39 \u500b\u82f1\u6587\u97f3\u7d20\u5b9a\u7fa9\u3002\u6b64\u65b9\u6cd5\u7d50\u5408\u4e2d\u82f1\uf978\u7a2e\u8a9e\u8a00\u4e4b\u97f3\u7d20\uff0c\u5efa\uf9f7\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u8072\u5b78\u6a21\u578b\u3002\u4f5c\u6cd5\u4e0a\u7684</td></tr><tr><td colspan=\"3\">\u7f3a\u9ede\uff0c\u5728\u65bc\u5404\u76ee\u6a19\u8a9e\u8a00\u4e2d\u76f8\u4f3c\u4e4b\u97f3\u7d20\uff0c\u6a21\u578b\uf96b\uf969\u7121\u6cd5\u5206\u4eab\uff0c\u800c\u4e14\u7576\u9700\u8981\u7d50\u5408\u7684\u76ee\u6a19\u8a9e\u8a00\u8b8a\u591a\u7684\u6642\u5019\uff0c\u6240\u9700</td></tr><tr><td colspan=\"2\">\u8981\u5b9a\u7fa9\u7684\u97f3\u7d20\u6a21\u578b\u6703\u5927\uf97e\u96a8\u4e4b\u589e\u52a0\u3002</td><td/></tr><tr><td colspan=\"2\">2.2. \u4ee5 IPA \u70ba\u57fa\u6e96\u5b9a\u7fa9\u591a\u8a9e\u97f3\u7d20</td><td/></tr><tr><td colspan=\"3\">\u7b2c\u4e8c\u7a2e\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9\u65b9\u5f0f\u662f\u57fa\u65bc\u5c08\u5bb6\u7684\u77e5\uf9fc\uff0c\u5c07\u500b\u5225\u7368\uf9f7\u7684\u55ae\u4e00\u8a9e\u8a00\u5c0d\u61c9\u5230 IPA \u6a19\u6e96\u7684\u7b26\u865f\u5b9a\u7fa9\uff0c\u85c9</td></tr><tr><td colspan=\"3\">\u6b64\u5404\u8a9e\u8a00\u9593\u53ef\u4ee5\u5206\u4eab\u76f8\u540c\u7684\u97f3\u7d20\u5b9a\u7fa9\u3002\u5982(\u8868 2)\u6240\u793a\u662f\u4ee5 IPA \u70ba\u6a19\u6e96\u4e4b\u4e2d\u82f1\u591a\u8a9e\u97f3\u7d20\u7684\u5b9a\u7fa9\u3002</td></tr><tr><td/><td>\u8868 2. \u4ee5 IPA \u70ba\u6a19\u6e96\u4e4b\u4e2d\u82f1\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9</td><td/></tr><tr><td>\u97f3\u7d20\uf9d0\u5225</td><td colspan=\"2\">IPA \u70ba\u6a19\u6e96\u4e4b\u4e2d\u82f1\u591a\u8a9e\u97f3\u7d20</td></tr><tr><td>\u6709\u8072\u7834\uf9a0\u97f3</td><td>B, D, G</td><td/></tr><tr><td>\u7121\u8072\u7834\uf9a0\u97f3</td><td>P, T, K</td><td/></tr><tr><td>\u6469\u64e6\u97f3</td><td>F, S, SH, H, X, V, TH, DH</td><td/></tr><tr><td>\uf96c\u64e6\u97f3</td><td>Z, ZH, C, CH, J, Q, CH, JH</td><td/></tr><tr><td>\u9f3b\u97f3</td><td>M, N, NG</td><td/></tr><tr><td>\uf9ca\u97f3</td><td>R, L</td><td/></tr><tr><td>\uf904\u97f3</td><td>W, Y</td><td/></tr><tr><td>\u524d\u90e8\u6bcd\u97f3</td><td>I, ER, V, EI, IH, EH, AE</td><td/></tr><tr><td colspan=\"3\">\u4e2d\u90e8\u6bcd\u97f3 \u578b\uff0c\u4e26\u914d\u5408\u591a\u8a9e\u8a9e\u8a00\u6a21\u578b\u548c\u591a\u8a9e\u767c\u97f3\u8fad\u5178\u6587\u6cd5\u6a39\uff0c\u9032\ufa08\uf99a\u7e8c\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u3002 ENG, AN, ANG, EN, AH, UH \u63a5\u4e0b\uf92d\u7684\u6587\u7ae0\u7d50\u69cb\u5c07\u5206\u5225\u63a2\u8a0e\u5982\u4e0b\uff1a\u7b2c\u4e8c\u7bc0\uff0c\u63a2\u8a0e\u904e\u53bb\u5c0d\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u7814\u7a76\u3002\u7b2c\u4e09\u7bc0\uff0c\uf96f\u660e\uf941\u6587\u65b9 \u80cc\u90e8\u5713\u5507\u6bcd\u97f3 O</td></tr><tr><td colspan=\"3\">\u6cd5\u5efa\uf9f7\ufa1d\u7c21\u6709\u6548\u7684\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\u65bc\u81ea\u52d5\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u61c9\u7528\u3002\u7b2c\u56db\u7bc0\uff0c\u91dd\u5c0d\u672c\uf941\u6587\u6240\u63d0\u65b9\u6cd5\u5efa\uf9f7\u4e4b\u591a\u8a9e\u97f3\u7d20\u6a21 \u80cc\u90e8\u975e\u5713\u5507\u6bcd\u97f3 A, U, OU, AI, AO, E, EE, OY, AW</td></tr><tr><td colspan=\"3\">\u578b\u9032\ufa08\u8fa8\uf9fc\u7d50\u679c\u8a55\u4f30\uff0c\u5be6\u9a57\u4e26\u8207\u4e4b\u524d\u65b9\u6cd5\u6bd4\u8f03\u3002\u7b2c\u4e94\u7bc0\u662f\u8a0e\uf941\uf96f\u660e\u8207\u7d50\uf941\u3002</td></tr><tr><td colspan=\"3\">(\u8868 2) \u5167\u4e4b\u97f3\u7d20\uf9d0\u5225\uf96b\u8003 Chomsky \u5b9a\u7fa9[10]\u3002\u5982\u6b64\u898f\u5247\u5730\u5c07\u4e2d\u82f1\uf978\u7a2e\u8a9e\u8a00\u7684\u97f3\u7d20\u7d50\u5408\uff0c\u5171\u8a08\u6709 52 \u500b\u4e2d\u82f1\u96d9 2. \u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u97f3\u7d20\u5b9a\u7fa9\u76f8\u95dc\u7814\u7a76 \u8a9e\u97f3\u7d20\u5b9a\u7fa9\u3002\u4f5c\u6cd5\u4e0a\u53ef\u4ee5\u6709\u6548\u5730\u5c07\u90e8\u5206\u7684\u4e2d\u82f1\u6587\u97f3\u7d20\u5408\u4f75\uff0c\u5171\u4eab\u8a9e\u8a00\u9593\u5f7c\u6b64\u7684\u5171\u540c\u97f3\u7d20\uff0c\u6e1b\u5c11\u8a9e\u97f3\u97f3\u7d20\u6a21</td></tr><tr><td colspan=\"3\">\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u97f3\u7d20\u5b9a\u7fa9\u7684\u65b9\u6cd5\uff0c\u4e3b\u8981\u53ef\u5206\u70ba\u4e09\u7a2e\u65b9\u5f0f\uff1a(\u4e00)\u76f4\u63a5\u7d50\u5408\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20\u5b9a\u7fa9\uff1b(\u4e8c) \u578b\u7684\u5b9a\u7fa9\u548c\u8a13\uf996\u3002\u4f46\u6b64\u4f5c\u6cd5\u7684\u7f3a\u9ede\u662f\u5efa\u69cb\u5728\u5c08\u5bb6\u77e5\uf9fc\u7684\u5206\u6790\uff0c\u800c\u975e\u5f9e\u8cc7\uf9be\u7279\u6027\u7d71\u8a08\u7684\u89d2\ufa01\u5b9a\u7fa9\u3002\u4e5f\u5c31\u662f\uf96f\uff0c</td></tr><tr><td colspan=\"3\">\u4f9d\u64da\u570b\u969b\u97f3\u7d20\u6a19\u6e96\u5b9a\u7fa9\uff0c\u627e\u51fa\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20\uf997\u96c6\uff1b(\u4e09)\u5f9e\u8cc7\uf9be\u5206\u6790\u7684\u89d2\ufa01(data-driven)\uff0c\u5408\u4f75\u500b\u5225\u55ae \u76f4\u63a5\u5c0d\u7167 IPA \u5b9a\u7fa9\u7522\u751f\u7684\u591a\u8a9e\u97f3\u7d20\u96c6\uff0c\u4e26\u6c92\u6709\u8003\u616e\u5230\u97f3\u7d20\u6a21\u578b\u9593\u983b\u8b5c\u7279\u6027\u3002\u5c08\u5bb6\u77e5\uf9fc\u5206\u6790\u7684\u591a\u8a9e\u97f3\u7d20\u96c6\uff0c</td></tr><tr><td colspan=\"3\">\u4e00\u8a9e\u8a00\u4e4b\u76f8\u4f3c\u97f3\u7d20\u3002\u73fe\u5206\u5225\u4ecb\u7d39\u5982\u4e0b\uff1a \u8207\u6700\u5f8c\u9032\ufa08\u8a9e\u97f3\u8fa8\uf9fc\uff0c\u5f9e\u8cc7\uf9be\u5206\u6790\u89d2\ufa01\u5efa\uf9f7\u7684\u7d71\u8a08\u6a21\u578b\u8a08\u7b97\uf967\u4e00\u81f4\u3002\u56e0\u6b64\uff0c\u63a1\u7528\u76f4\u63a5\u5c0d\u7167 IPA \u5b9a\u7fa9\u4e4b\u591a\u8a9e</td></tr><tr><td colspan=\"2\">2.1. \u76f4\u63a5\u7d50\u5408\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20 \u97f3\u7d20\u6a21\u578b\u4e26\uf967\u80fd\u78ba\u5be6\u5730\u5448\u73fe\u7d71\u8a08\u8a13\uf996\u8cc7\uf9be\u4e0a\u7684\u5206\u4f48\u3002</td><td/></tr><tr><td colspan=\"2\">\u5982(\u8868 1)\u6240\u793a\uff0c\u6bd4\u7167\u4e2d\u6587\u548c\u82f1\u6587\u55ae\u4e00\u8a9e\u8a00\u97f3\u7d20\u7684\u5b9a\u7fa9\u3002 2.3. \uf97e\u6e2c\u97f3\u7d20\u76f8\u4f3c\ufa01\u5b9a\u7fa9\u591a\u8a9e\u97f3\u7d20\u96c6</td><td/></tr><tr><td colspan=\"3\">\u9664\uf9ba\u76f4\u63a5\u6df7\u5408\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9\uff0c\u4ee5\u53ca\uf9dd\u7528 IPA \u570b\u969b\u6a19\u6e96\u5b9a\u7fa9\u7684\u591a\u8a9e\u97f3\u7d20\uff0c\u904e\u53bb\u7814\u7a76\u4e5f\u66fe\uf9dd\u7528\u4f30\u6e2c\u4e09\uf99a\u97f3 \u8868 1. \u7d50\u5408\u4e2d\u82f1\u6587\u97f3\u7d20\u5b9a\u7fa9 \u7d20\u6a21\u578b\u9593\u7684\u76f8\u4f3c\ufa01\uff0c\u4ee5 HMM \u6a21\u578b\uf96b\uf969\u8ddd\uf9ea\u8a08\u7b97\uff0c\uf9dd\u7528\u905e\u8ff4\u65b9\u6cd5\u5408\u4f75\u4e09\uf99a\u97f3\u7d20\u6a21\u578b (triphone)\uff0c\u5efa\u69cb\u51fa\u591a</td></tr><tr><td colspan=\"3\">\u97f3\u7d20\uf9d0\u5225 \u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u7684\u97f3\u7d20\u96c6[8][9]\u3002\uf978\u500b\u9ad8\u65af\u5206\u4f48\u7684\u76f8\u4f3c\u53ef\u4ee5\uf9dd\u7528\u5e73\u5747\u503c\u548c\u8b8a\uf962\uf969\u51fd\uf969\uff0c\uf92d\u63cf\u8ff0\u5f7c\u6b64\u7684\u76f8\u4f3c\u7a0b\ufa01\u3002 \u4e2d\u6587 \u82f1\u6587 \u6709\u8072\u7834\uf9a0\u97f3 b_M, d_M, g_M b, d, g \uf9dd\u7528 Bhattacharyya distance [8]\uf92d\u8a08\u7b97\u97f3\u7d20\u6a21\u578b\u9593\u7684\u8ddd\uf9ea \uff0c\u8868\u793a\u5982\u4e0b\uff1a bha</td></tr><tr><td>\u7121\u8072\u7834\uf9a0\u97f3</td><td>p_M, t_M, k_M</td><td>p, t, k</td></tr><tr><td>\u6469\u64e6\u97f3</td><td>f_M, s_M, sh_M, h_M, x_M</td><td>f, v, th, dh, s, sh, hh</td></tr><tr><td>\uf96c\u64e6\u97f3</td><td>c_M, ch_M, j_M, q_M, z_M, zh_M</td><td>ch, jh, z, zh</td></tr><tr><td>\u9f3b\u97f3</td><td>m_M, n_M</td><td>m, n, ng</td></tr><tr><td>\uf9ca\u97f3</td><td>r_M, l_M</td><td>r, l</td></tr><tr><td>\uf904\u97f3</td><td/><td>w, y</td></tr><tr><td>\u524d\u90e8\u6bcd\u97f3</td><td>i_M, v_M, ei_M, er_M</td><td>ih, eh, ae, iy, ey</td></tr><tr><td>\u4e2d\u90e8\u6bcd\u97f3</td><td>an_M, ang_M, en_M, eng_M</td><td>ah, uh, er</td></tr><tr><td>\u80cc\u90e8\u5713\u5507\u6bcd\u97f3</td><td>o_M</td><td>ao</td></tr></table>",
"num": null
},
"TABREF3": {
"type_str": "table",
"html": null,
"text": "\u9ea5\u514b\u98a8\u8a9e\uf9be\uf93f\u88fd 16KHz \u53d6\u6a23\u983b\uf961 16bits \u7684\u53d6\u6a23\u9ede\u97f3\u6a94\uff0c\u96fb\u8a71\u8a9e\uf9be\uf93f\u88fd 8KHz \u53d6\u6a23\u983b\uf961 16bits \u7684\u53d6\u6a23\u9ede\u97f3\u6a94\uff0c \u5176\u4e2d\u96fb\u8a71\u8a9e\uf9be\u53c8\u53ef\u7d30\u5206\u70ba\u56fa\u5b9a\u5f0f\u96fb\u8a71(PSTN)\u8a9e\uf9be\u53ca\ufa08\u52d5\u96fb\u8a71(GSM )\u8a9e\uf9be\uff0c\u96fb\u8a71\u8a9e\uf9be\u90e8\u4efd\u662f\u900f\u904e Dialogic \u96fb \u8a71\u8a9e\u97f3\u4ecb\u9762\u5361\uff0c\uf93f\u5f97\u7684 8KHz\uff0c8Bits\uff0cMulaw \u683c\u5f0f\u7684\u53d6\u6a23\u9ede,\u7d93\u7a0b\u5f0f\u8f49\u6210 8KHz\uff0c16bits\uff0cpcm \u683c\u5f0f\u7684\u53d6\u6a23\u9ede\uff1b \u9ea5\u514b\u98a8\u8a9e\uf9be\u662f\u7531\u500b\u4eba\u96fb\u8166\u53ca\u9ea5\u514b\u98a8\uff0c\u76f4\u63a5\u5f9e\u500b\u4eba\u96fb\u8166\u7684\u97f3\u6548\u5361\uf93f\u88fd 16KHz\uff0c16bits \u7684\u8072\u97f3\u8a0a\u865f\u3002\u6700\u5f8c\u5c07\u6240 \u6709\u53d6\u6a23\u9ede\u4ee5 \u8868\u793a\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9\u7684 HMM \u6a21\u578b\uff0c\u7814\u7a76\u4e0a\u61c9\u7528 3 \u500b\uf9fa\u614b(state)\uf92d\u63cf\u8ff0\u6bcf\u4e00\u500b HMM \u6a21\u578b\uff0c\u6bcf \u4e00\u500b\uf9fa\u614b\u5305\u542b\u6709 16 \u500b\u9ad8\u65af(mixture)\u3002\u6b64\u591a\u8a9e\u4e4b\u6a39\uf9fa\u7d50\u69cb\u767c\u97f3\u8fad\u5178\u8209\uf9b5\u5171\u6709\uff1asay(sil_S_EY, S_EY_sil)\u3001\u5df4\uf989 (sil_B_IY, B_IY_L, B_L_IY, L_IY_sil)\u3001top(sil_T_AA, T_AA_P, AA_P_sil)\u7b49\u8a5e\u7d44\u3002\u672c\u5be6\u9a57\u5408\u4f75\u82f1\u6587\u767c\u97f3\u8fad\u5178 \u8207\u4e2d\u6587\u767c\u97f3\u8fad\u5178\uff0c\u5efa\uf9f7\u5305\u542b 29,104 \u500b\u4e2d\u82f1\u6587\u8a5e\u4e4b\u591a\u8a9e\u767c\u97f3\u8fad\u5178\u3002\u672c\u5716\u793a\u8209\uf9b5\uf96f\u660e\u7531\u975c\u97f3(silence, sil)\u70ba\u8d77\u9ede\uff0c \u8fa8\uf9fc\u591a\u8a9e\u8a9e\uf906\"say (sil_S_EY, S_EY_sil) \u5df4\uf989 (sil_B_IY, B_IY_L, IY_L_IY, L_IY_sil)\"\u70ba\uf9b5\uff0c \u70ba\u6a39\u7684\u6839\u7bc0 \uf9dd\u7528\u524d\u5f8c\u6587\u8108\u5206\u6790\u65b9\u6cd5 HAL \u6bd4\u8072\u5b78\u76f8\u4f3c\ufa01\u65b9\u6cd5 ACL \u6709\u8f03\u9ad8\u7684\u6e96\u78ba\uf961\uff0c\u800c\u540c\u6642\u7d50\u5408\u8072\u5b78\u76f8\u4f3c\ufa01\u8207\u524d\u5f8c\u6587\u8108 \u5206\u6790 FUN \u53ef\u4ee5\u6709\u6700\u4f73\u7684\u8fa8\uf9fc\u6548\u679c\u3002\u7576\u7fa4\u96c6\uf969 16 Y = \u6642\uff0c\uf941\u6587\u6240\u63d0\u4e4b\u65b9\u6cd5(FUN)\u53ef\u4ee5\u6709\u6700\u597d\u7684\u8fa8\uf9fc\u6548\u679c\u3002\u56e0 \u6b64\uff0c\uf941\u6587\u8a2d\u5b9a\u7fa4\u96c6\u5206\u6790 16 Y = \uff0c\u7fa4\u96c6\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\uff0c\u5206\u6790\u5982(\u5716 5)\u6240\u793a\u3002\u7d93\u904e\u5206\uf9d0\u5b8c\u5f8c\uff0c\u5404\u500b IPA \u5b9a\u7fa9\u4e4b\u97f3 \u7d20\u4e2d\uff0c\u6240\u5305\u542b\u4e4b\u4e09\uf99a\u97f3\u500b\uf969\u3002\u7531\u4e0a\u5716\u53ef\u77e5\uff0c\u4ee5 55 \u500b IPA \u6a19\u6e96\u5b9a\u7fa9\u6240\u7522\u751f\u4e4b 997 \u500b\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\uff0c\uf9dd\u7528\u8cc7 \uf9be\u878d\u5408\u65b9\u6cd5\u53ef\u4ee5\u5408\u4f75\u70ba 260 \u500b\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\u3002",
"content": "<table><tr><td colspan=\"4\">\u900f\u904e\u591a\u8a9e\u767c\u97f3\u8fad\u5178\uff0c\u53ef\u4ee5\u5efa\u69cb\u51fa\u591a\u8a9e\u767c\u97f3\u4e4b\u6587\u6cd5\u6a39(grammar tree) [20]\u3002\u5982\u4e0b(\u5716 4)\u6240\u793a\u3002\u5728\u8fa8\uf9fc\u7684\uf9ca\u7a0b\u4e0a\uff0c</td></tr><tr><td colspan=\"4\">wav \u683c\u5f0f\u97f3\u6a94\u5132\u5b58\u3002\u672c\uf941\u6587\u7814\u7a76\u63a1\u7528\u9ea5\u514b\u98a8\u8a9e\uf9be\u90e8\u5206\u3002 \u6bcf\u4f4d\u8a9e\u8005\u6536\uf93f 80 \uf906\u8a9e\u97f3\u8a9e\uf9be\uff0c\u8a9e\uf9be\u5167\u5bb9\u8a2d\u8a08\u6709\u82f1\u6587\uf969\u5b57\uf99a\u7e8c\u8a9e\u97f3\u3001\u82f1\u6587\u5b57\u6bcd\uf99a\u7e8c\u8a9e\u97f3\u3001\u4e2d\u82f1\u6587\u6df7\u5408 \uf906\u3001\u82f1\u6587\u55ae\u5b57\u3001\u7247\u8a9e\u6216\uf906\u5b50\u7b49\uff0c\u5982(\u8868 5)\u6240\u793a\u3002\uf941\u6587\u4e3b\u8981\u63a2\u8a0e\u4e2d\u82f1\u593e\u96dc\u7684\u591a\u8a9e\u61c9\u7528\uff0c\u5be6\u9a57\u62bd\u53d6\u8a9e\uf9be\u5167\u4e2d\u82f1\u6587 \u6df7\u5408\uf906\u578b (\u8868 5 \u4e4b 6 \u548c 7)\uff0c\u8a9e\uf9be\u7de8\u865f#58 \u81f3#70 \u7684\u97f3\u6a94\u8cc7\u8a0a\u3002 4.4. \u8072\u5b78\u8207\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u5206\u6790\u65bc\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc \u6bcf\u4e00\u500b\u5206\u652f(arc)\u9ede\uff1b \u7dda\u689d\u8868\u793a\u591a\u8a9e\u97f3\u7d20\u4e5f\u5c31\u662f\u8a13\uf996\u7684\u8072\u5b78\u6a21\u578b\uff0c \u6307\u97f3\u7d20\u7684\u7bc0\u9ede\uff1b \u8868\u793a\uf96e\u7d50\u9ede\uff0c\u6307\u51fa\u5f9e\u6839\u7bc0\u9ede\u5230\u6b64 \u672c\uf941\u6587\u7814\u7a76\u63a2\u8a0e\u4e2d\u6587\u548c\u82f1\u6587\u7684\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u61c9\u7528\uff0c\u5be6\u9a57\u9996\u5148\u6e2c\u8a66\u4f7f\u7528\u55ae\u97f3\u7d20\u6a21\u578b(monophone)\u7684\u5b9a</td></tr><tr><td colspan=\"4\">\uf96e\u7d50\u9ede\u4e4b\u767c\u8072\u97f3\u7d20\u53ef\u80fd\u69cb\u6210\u7684\u6240\u6709\u591a\u8a9e\u8a5e\u5f59\uff1b \u7fa9\uff0c\u4f9d\u64da(\u8868 1)\u548c(\u8868 2)\u7b49\uf967\u540c\u6a19\u8a18\u65b9\u6cd5\u7684\u5167\u5bb9\uff0c\u5206\u5225\u53ef\u4ee5\u5b9a\u7fa9\uff1a (\u4e00) \u76f4\u63a5\u7d50\u5408\u500b\u5225\u55ae\u4e00\u8a9e\u8a00\u4e4b\u97f3\u7d20(MIX)\uff1b \u8868\u793a\u6a39\u8207\u6a39\u4e4b\u9593\uf99a\u7d50\u7684\u8a9e\u8a00\u6a21\u578b\u3002 \u8868 5. EAT \u8a9e\uf9be\u4e2d\u591a\u8a9e\uf906\u578b\u7bc4\uf9b5 4.3. \uf9dd\u7528\u8072\u5b78\u8207\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u5206\u6790\u7fa4\u805a\u4e09\uf99a\u97f3\u7d20\u6a21\u578b (\u4e8c) \u4ee5 IPA \u70ba\u57fa\u6e96\u5b9a\u7fa9\u591a\u8a9e\u97f3\u7d20\u4e4b\u65b9\u6cd5 (IPA)\u3002\u5be6\u9a57\u7d50\u679c\u5982(\u8868 7)\u6240\u793a\uff1a</td></tr><tr><td colspan=\"4\">EAT \u8a9e\uf9be\uf906\u578b 100% four eight three zero one two nine for instance Safe len ins del sub len \u2212 \u2212 \u00d7 \u2212 \u8a9e\u97f3\u8fa8\uf9fc\u53ef\u80fd\u767c\u751f\u7684\u932f\u8aa4\u6709\u4e09\u7a2e\u578b\u614b\uff0c\u5206\u5225\u662f\u63d2\u5165\u932f\u8aa4(insertion)\u3001\u522a\u9664\u932f\u8aa4(deletion)\u4ee5\u53ca\u66ff\u63db\u932f\u8aa4 1 2 3 Accuracy = (\u5f0f 9) (substitution)\u3002\u5be6\u9a57\u4e2d\u97f3\u7d20\u6b63\u78ba\uf961(accuracy)\u7684\u8a08\u7b97[21]\uff0c\u65b9\u5f0f\u5982\u4e0b\uff1a \u8868 7. \u55ae\u97f3\u7d20\u6a21\u578b\u4e4b\u591a\u8a9e\u8fa8\uf9fc\u97f3\u7d20\u6b63\u78ba\uf961 (\u62ec\u5f27\u5167\u8868\u8fa8\uf9fc\u55ae\u5143\u4e4b\u500b\uf969)</td></tr><tr><td colspan=\"3\">4 \u5176\u4e2d\uff0c le \u70ba\u8fa8\uf9fc\u7d50\u679c\uff0c\u97f3\u7d20\u5e8f\uf99c\u7684\u9577\ufa01\u3002 in \u70ba\u6bd4\u8f03\u8f03\u6b63\u78ba\u7d50\u679c\u591a\u8fa8\uf9fc\u51fa\u7684\u97f3\u7d20\uff0c\u5c6c\u65bc\u63d2\u5165\u932f\u8aa4\uff0c Silicon Graphics n s</td><td>del</td><td>\u70ba</td></tr><tr><td colspan=\"4\">5 \u6bd4\u8f03\u6b63\u78ba\u7d50\u679c\u5c11\u8fa8\uf9fc\u5230\u7684\u97f3\u7d20\uff0c\u5c6c\u65bc\u522a\u9664\u932f\u8aa4\u3002 R. S. R. T. E. K. 6 \u6790\uf967\u540c\u7fa4\u96c6\u689d\u4ef6\u4e0b\u7684\u7fa4\u805a\u97f3\u7d20\u500b\uf969\uff0c\uf9dd\u7528\u8abf\u6574 k \u7fa4\u805a(modified k-means, MKM)\u5206\uf9d0\u65b9\u6cd5[16]\uff0c\u7fa4\u805a\u4e09\uf99a\u97f3 sub \u70ba\u6bd4\u8f03\u6b63\u78ba\u7d50\u679c\u8fa8\uf9fc\u932f\u8aa4\u7684\u97f3\u7d20\uff0c\u5c6c\u65bc\u66ff\u63db\u932f\u8aa4\u3002\u5206 \u51a0\u8ecd\u5bb6\u5ead T.V.\u79c0\u5165\u570d\uf90a\u9418\u734e 7 \u7d20\u6a21\u578b\u70ba\u6709\u6548\u591a\u8a9e\u8fa8\uf9fc\u6a21\u578b\u3002\u5be6\u9a57\u8072\u5b78\u76f8\u4f3c\ufa01\u8a08\u7b97(ACL)\u3001\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u8a08\u7b97(HAL)\u53ca\u8cc7\uf9be\u878d\u5408\u6280\u8853 \u5e6b\u6211\u67e5\u4e00\u4e0b Bryan \u7684\u5206\u6a5f (FUN)\u7b49\uf967\u540c\u65b9\u6cd5\uff0c\u5728\u6536\u6582\u9580\u6abb\u503c\u70ba 0.01 \u03b8 = \u7684\u60c5\u6cc1\u4e0b\uff0c\u5be6\u9a57\uf967\u540c\u6700\u5927\u7fa4\u96c6\uf969 Y \u3002(\u8868 6)\u5be6\u9a57\u5206\u6790\u5404\u7a2e\uf967\u540c</td></tr><tr><td colspan=\"3\">8 \u65b9\u6cd5\u7fa4\u96c6\u4e4b\u591a\u8a9e\u97f3\u7d20\u500b\uf969\u53ca\u97f3\u7d20\u8fa8\uf9fc\u7684\u6b63\u78ba\uf961\uff0c\u5982\u4e0b\u6240\u793a\uff1a The vote at the September meeting was eleven zero</td></tr><tr><td colspan=\"4\">\u539f\u672c\u97f3\u6a94\u5167\u5bb9\u7686\u5c6c\u65bc raw \u683c\u5f0f\uff0c\u56e0\u6b64\u6211\u5011\u4e8b\u5148\u5c0d\u97f3\u6a94\u4f5c dc-offset \u53ca silence removal \u7684\u8655\uf9e4\u3002\u4e26\u4e14\u6839\u64da\u82f1\u6587 \u8868 6. \uf967\u540c\u7fa4\u96c6\uf969\u76ee\u9650\u5236\u689d\u4ef6\u4e0b\u7fa4\u805a\u97f3\u7d20\u500b\uf969\u53ca\u8fa8\uf9fc\u6b63\u78ba\uf961 (Y :\u6700\u5927\u7fa4\u96c6\uf969\u76ee, 0.01 \u03b8 = )</td></tr><tr><td colspan=\"4\">) \u767c\u97f3\u8fad\u5178\u8207\u4e2d\u6587\u767c\u97f3\u8fad\u5178\uff0c\u5c07\u6587\u5b57\u8a3b\u89e3\u8f49\u6210\u97f3\u7d20\u6a19\u8a18\u3002\u7531\u65bc\u8a9e\uf9be\u5167\u6709\u90e8\u4efd\u97f3\u6a94\u53ca\uf93f\u97f3\u54c1\u8cea\uf967\uf97c\uff0c\u5be6\u9a57\u4ee5\u4eba \u5de5\u7684\u65b9\u5f0f\u5148\ufa08\u6821\u5c0d\u3002\u6700\u5f8c\uff0c\uf941\u6587\u6240\u63a1\u7528\u4e4b\u5be6\u9a57\u8a9e\uf9be\u5305\u542b\u6709\u8a13\uf996\u7528\u4e2d\u82f1\u6587\u6df7\u5408\uf906\u578b\u5171\u6709 2,018 \uf906\uff0c\u5be6\u9a57\u8a55\u4f30 8 Y = 16 Y = 32 Y =</td></tr><tr><td colspan=\"4\">\u5176\u4e2d\uff0c\u5411\uf97e l s \u8868\u793a\u76ee\u524d\u76f8\u4f3c\ufa01\u77e9\u9663\u5728\ufa08\uf96a\u5f15 l \u7684\u97f3\u7d20\uff0c\u5411\uf97e k s \u8868\u793a\u76f8\u4f3c\ufa01\u77e9\u9663\u5728\uf99c\uf96a\u5f15 \u7684\u97f3\u7d20\uff0c\u5168\u90e8\u97f3 \u7d20\u7e3d\u5171\u6709 n \u540d\u3002\u672c\u7814\u7a76\uf9dd\u7528\u8abf\u6574\u6027 k \u7fa4\u805a(modified k-means, MKM)\u5206\uf9d0\u65b9\u6cd5[16]\uff0c\u5b9a\u7fa9\u6536\u6582\u689d\u4ef6\u70ba\u5206\u7fa4\u5167 \u7684\u8cc7\uf9be\u8b8a\uf962\ufa01\u4f4e\u65bc\u5b9a\u7fa9\u4e4b\u9580\u6abb\u503c\uff0c\u5247\u9054\u6210\u5206\u7fa4\u7d42\u6b62\uff0c\u6700\u5f8c\u5b8c\u6210\uf941\u6587\u6240\u63d0\u4e4b\u6709\u6548\u591a\u8a9e\u97f3\u7d20\u96c6\uff0c\u5176\u4e2d\u6536\u6582\u689d\u4ef6 \u70ba\uff1a \u6e2c\u8a66\u5171\u6709 100 \uf906\u3002 \u6b63\u78ba\uf961 \u97f3\u7d20\u500b\uf969 \u6b63\u78ba\uf961 \u97f3\u7d20\u500b\uf969 \u6b63\u78ba\uf961 \u97f3\u7d20\u500b\uf969 k 1 1 1 1 1 ( ) / Y Y 4.2. \u97f3\u7d20\u70ba\u57fa\u6e96\u4e4b\u81ea\u52d5\u8a9e\u97f3\u8fa8\uf9fc\u67b6\u69cb ACL 62.22% 161 63.12% 288 64.37% 531 \u70ba\uf9ba\u8a55\u4f30\u97f3\u7d20\u5b9a\u7fa9\u7684\u597d\u58de\uff0c\u672c\uf941\u6587\u4f7f\u7528\u81ea\ufa08\u958b\u767c\u7684\u591a\u8a9e\u97f3\u7d20\u8fa8\uf9fc\u7cfb\u7d71\uff0c\u63a2\u8a0e\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u3002\u63a1\u7528\u4e0a\u8ff0 HAL 62.52% 159 64.23% 286 64.57% 530 \u4e4b\u5be6\u9a57\u8a9e\uf9be\u4e2d\uff0c\u6211\u5011\uf9dd\u7528 IPA \u97f3\u7d20\u6a19\u6e96\u5b9a\u7fa9\uff0c\u627e\u51fa\u591a\u8a9e\u97f3\u7d20\u4e4b\uf997\u96c6\u3002\u5b9a\u7fa9\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\u5171 N=997 \u500b\uff0c\u8a13\uf996 FUN 64.44% 119 66.07% 260 64.74% 515 \u8a9e\uf9be\u5c11\u65bc 5 \u6b21\u7684\u4e09\uf99a\u97f3\u7d20\uf967\u4e88\u8003\u616e\u3002\u5728\u8a9e\u97f3\uf96b\uf969\u64f7\u53d6\u7684\u90e8\u4efd\uff0c\u5c0d\u65bc\u8f38\u5165\u7684\u8a9e\u97f3\u8a0a\u865f\u8a08\u7b97 26 \u7dad\u7684\u6885\u723e\u5012\u983b Y t t t y y y y y y \u03b8 \u2212 \u2212 = = = \u0394 \u2212 \u0394 \u0394 < \u2211 \u2211 \u2211 (\u5f0f 8) \u5176\u4e2d\uff0c \u8868\u793a\u5728\u7b2c \u6b21\u905e\u8ff4\u4e2d\uff0c \u7fa4\u96c6\u4e2d\u7b2c \u7fa4\u4e4b\u96c6\u5408\u5167\u500b\uf969\u5206\uf969\u503c t y \u0394 t Y y ( , ) t y l k c s s \u0394 = \u2211 \uff0c \u8868\u793a\u904b \u7b97\u905e\u8ff4\u6b21\uf969\uff0c \u6307\u8a2d\u5b9a\u4e4b\u6700\u5927\u905e\u8ff4\u6b21\uf969\uff0c max 1,..., t t \u8b5c\uf96b\uf969(mel-frequency ceptral coefficient, MFCC)\uff0c\u5176\u4e2d\u5305\u542b 12 \u968e\u7684\u6885\u723e\u5012\u983b\u8b5c\uf96b\uf969\uff0c\u52a0\u4e0a 12 \u968e\u7684\u4e00\u6b21\u5fae\u5206 \u5be6\u9a57\u8003\u616e\u8072\u5b78\u76f8\u4f3c\ufa01\u77e9\u9663\u5206\uf969\u8a08\u7b97\uff0c\uf9dd\u7528\u8072\u5b78\u76f8\u4f3c\ufa01\u7fa4\u805a\u65b9\u6cd5 ACL\uff0c\u5728 8, 16, 32 Y = \u7684\u60c5\u6cc1\u4e0b\uff0c\u5206\u5225\u53ef\u4ee5 \u6885\u723e\u5012\u983b\u8b5c\uf96b\uf969\uff0c\u4ee5\u53ca\u4e00\u968e\u7684\u80fd\uf97e\u548c\u5176\u4e00\u6b21\u5fae\u5206\uf96b\uf969\uff0c\u4e26\u4e14\u5c0d\uf96b\uf969\u505a MVA [18]\u8655\uf9e4\u4ee5\u589e\u52a0\u8fa8\uf9fc\u7684\u5f37\u5065\u6027\u3002 \u7fa4\u805a\u70ba 161\uff0c288 \u53ca 531 \u500b\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\uff0c\u5176\u97f3\u7d20\u8fa8\uf9fc\u6b63\u78ba\uf961\u5206\u5225\u70ba 62.22%\uff0c63.12%\u53ca 64.37%\u3002\uf9dd\u7528\u8a9e\u8a00 = max t \u8d85\u7a7a\u9593\u5206\u6790\u65b9\u6cd5 HAL\uff0c\u5728 \u7684\u60c5\u6cc1\u4e0b\uff0c\u5206\u5225\u53ef\u4ee5\u7fa4\u805a\u70ba 159\uff0c286 \u53ca 530 \u500b\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\uff0c\u5176 8, 16, 32 Y = \u03b8 \u70ba\u6536\u6582\u4e4b\u9580\u6abb\u503c\u3002 \u97f3\u7d20\u8fa8\uf9fc\u6b63\u78ba\uf961\u5206\u5225\u70ba 62.52%\uff0c64.23%\u53ca 64.57%\u3002\uf9dd\u7528\u8cc7\uf9be\u878d\u5408\u65b9\u6cd5 FUN\uff0c\u5728 \u7684\u60c5\u6cc1\u4e0b\uff0c 8, 16, 32 Y =</td></tr><tr><td colspan=\"4\">\u5206\u5225\u53ef\u4ee5\u7fa4\u805a\u70ba 159\uff0c286 \u53ca 530 \u500b\u591a\u8a9e\u97f3\u7d20\u6a21\u578b\uff0c\u5176\u97f3\u7d20\u8fa8\uf9fc\u6b63\u78ba\uf961\u5206\u5225\u70ba 64.44%\uff0c66.07%\u53ca 64.74%\u3002 4. \u5be6\u9a57\u8a55\u4f30 \u70ba\uf9ba\u8a55\u4f30\u7814\u7a76\u65b9\u6cd5\uff0c\uf941\u6587\u63d0\u51fa\u5e7e\u9805\u5be6\u9a57\u9a57\u8b49\uff1a\u9996\u5148\uff0c\u5be6\u9a57\u55ae\u7368\u8003\u616e\u8072\u5b78\u76f8\u4f3c\ufa01\u3001\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u8207 16</td></tr><tr><td colspan=\"4\">\u672c\uf941\u6587\u6240\u63d0\u7d50\u5408\u8072\u5b78\u8207\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u5206\u6790\u4e4b\u65b9\u6cd5\uff0c\u6bd4\u8f03\u5176\u8fa8\uf9fc\u7d50\u679c\u3002\u518d\u8005\uff0c\u6bd4\u8f03\u8207\u524d\u5f8c\u6587\u8108\u7368\uf9f7\u4e4b\u97f3 14</td></tr><tr><td colspan=\"4\">\u7d20\u96c6\u548c\uf941\u6587\u6240\u63d0\u8207\u524d\u5f8c\u6587\u8108\u76f8\u95dc\u4e4b\u97f3\u7d20\u96c6\u5728\u591a\u8a9e\u8fa8\uf9fc\u6e96\u78ba\uf961\u7684\u5dee\u5225\u3002 4.1. \u591a\u8a9e\u8a9e\u97f3\u8a9e\uf9be\u5206\u6790 \u672c\uf941\u6587\u4f7f\u7528\u7684\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u8a13\uf996\u8a9e\uf9be\uff0c\u53f0\u7063\u8154\u82f1\u6587(English Across Taiwan, EAT)\u8a9e\uf9be\u5eab\uff0c\u5176\u4e2d\u5305\u542b\u82f1 \u6587\u9577\uf906,\u82f1\u6587\u77ed\uf906,\u82f1\u6587\u55ae\u8a5e\u53ca\u4e2d\u82f1\u593e\u96dc\uf906\u7b49[17]\u3002\u5f9e 2004 \uf98e 5 \u6708\u958b\u59cb\u6536\u96c6\uff0c\u81f3 2005 \uf98e 1 \u6708\u521d\u6b65\u5b8c\u6210\u6536\u96c6\uff0c \u7531\u5e2b\u5927\u3001\u4ea4\u5927\u3001\u6e05\u5927\u3001\u6210\u5927\u548c\u53f0\u5927\u7b49\u4e94\u6240\u5b78\u6821\uf96b\u8207\u8a9e\uf9be\u4e4b\uf93f\u88fd\u6536\u96c6\uff0c\u7d93\u5de5\u7814\u9662\u96fb\u901a\u6240\u5f59\u6574\u3002\u5206\u5225\u7531\u82f1\u8a9e\u7cfb \u53ca\u975e\u82f1\u8a9e\u7cfb\u5b78\u751f\uf93f\u88fd\uff0c\u8a9e\uf9be\u4f9d\u6027\u5225\u505a\u5206\uf9d0\uff0c\uf93f\u88fd\u6709\u9ea5\u514b\u98a8\u8a9e\uf9be\u53ca\u96fb\u8a71\u8a9e\uf9be\uff0c\u6b78\u7d0d\u5982\u4e0b\u8868\u6240\uf99c\uff1a \u8868 4. EAT \u8a9e\uf9be\u9ea5\u514b\u98a8\u97f3\u6a94\u8cc7\uf9be\u7d71\u8a08 MIC 16khz 16bits \u8a9e\uf9be 4 6 8 10 Number of clustered tri-phones 12</td></tr><tr><td>2</td><td>\u82f1\u8a9e\u7cfb</td><td>\u975e\u82f1\u8a9e\u7cfb</td></tr><tr><td colspan=\"4\">\u7537\u6027 11,977 \u5716 4. \uf9dd\u7528\u6a39\uf9fa\u7d50\u69cb\u767c\u97f3\u8fad\u5178\u6587\u6cd5\u6a39\u65bc\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u67b6\u69cb\u5716 \uf981\u6027 \u7537\u6027 \uf981\u6027 \uf906\uf969 30,094 25,432 0 10 20 30 40 50 15,540 \u4eba\uf969 166 406 368 \u672c\uf941\u6587\u8abf\u6574\u4e00\u822c\u8a9e\u97f3\u8fa8\uf9fc\u4f7f\u7528\u7684\u8a9e\u8a00\u6a21\u578b(language model)\uff0c\u5728\u8a08\u7b97\u4e0a\uf9dd\u7528\u5747\u7b49\u6a5f\uf961(equal probability) IPA phone model 224 \u7684\u65b9\u6cd5[19]\uff0c\u78ba\u4fdd\u53ef\u4ee5\u771f\u6b63\u5448\u73fe\uf967\u540c\u591a\u8a9e\u97f3\u7d20\u5b9a\u7fa9\u7684\u8072\u5b78\u6a21\u578b(acoustic model)\uff0c\u5c0d\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u7684\u5f71\u97ff\u3002\u5728 \u591a\u8a9e\u8a9e\u97f3\u97f3\u7d20\u8fa8\uf9fc\uff0c\u9700\u8981\u4f9d\u64da\u5b9a\u7fa9\u7684\u591a\u8a9e\u97f3\u7d20\u7d50\u5408\u5404\u500b\u76ee\u6a19\u8a9e\u8a00\u7684\u767c\u97f3\u8fad\u5178\uff0c\u5efa\u69cb\u51fa\u4e00\u500b\u591a\u8a9e\u767c\u97f3\u8fad\u5178\u3002 \u5716 5. \uf9dd\u7528\u8072\u5b78\u76f8\u4f3c\ufa01\u53ca\u524d\u5f8c\u6587\u8108\u5206\u6790\u7fa4\u96c6\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\u5206\u4f48\u5716\uff0c 16 Y =</td></tr></table>",
"num": null
},
"TABREF5": {
"type_str": "table",
"html": null,
"text": "Triphone sets)\u7684\u591a\u8a9e\u8fa8\uf9fc\u6548\u679c\u53ef\u9054 68.07%\u7684\u6b63\u78ba\uf961\u3002\uf9dd\u7528\u8072\u5b78\u76f8 \u4f3c\ufa01\u77e9\u9663\u7fa4\u96c6\u97f3\u7d20\u5b9a\u7fa9(ACL phone sets) \uff0c\u5728\u591a\u8a9e\u8fa8\uf9fc\u6548\u679c\u4e0a\u53ef\u9054 63.12%\u7684\u6b63\u78ba\uf961\uff0c\u800c\uf9dd\u7528\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8 \u4f3c\ufa01\u77e9\u9663\u7fa4\u96c6\u97f3\u7d20\u5b9a\u7fa9(HAL phone sets) \uff0c\u5728\u4e2d\u82f1\u6587\u591a\u8a9e\u8fa8\uf9fc\u6548\u679c\u4e0a\u53ef\u9054 64.23%\u7684\u6b63\u78ba\uf961\u3002\u9032\u4e00\u6b65\uf9dd\u7528\u8cc7 \uf9be\u878d\u5408\u65b9\u6cd5\u65bc\u8072\u5b78\u53ca\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u77e9\u9663\u7fa4\u96c6\u5206\u6790\u4e4b\u97f3\u7d20\u5b9a\u7fa9(FUN phone sets) \uff0c\u5728\u591a\u8a9e\u8fa8\uf9fc\u6548\u679c\u53ef\u4ee5 \u63d0\u5347\u81f3 66.07%\u7684\u6b63\u78ba\uf961\u3002\u6574\u9ad4\u800c\u8a00\uff0c\u63a1\u7528\u4e09\uf99a\u97f3\u7d20\u7684\u8fa8\uf9fc\u6548\u679c\u6bd4\u55ae\u97f3\u7d20(IPA \u6216 MIX)\u5b9a\u7fa9\u597d\u3002\u53c8\u5f9e\u8a9e\u8a00\u5206 \u6790(HAL)\u6548\u679c\u6703\u8f03\u8072\u5b78\u5206\u6790(ACL)\u6548\u679c\uf92d\u5f97\u986f\u8457\uff0c\u4e14\uf9dd\u7528\u8cc7\uf9be\u878d\u5408\u65b9\u6cd5\u7d50\u5408\u8072\u5b78\u76f8\u4f3c\ufa01\u53ca\u524d\u5f8c\u6587\u8108\u5206\u6790\uff0c\u5c0d \u65bc\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u53ef\u4ee5\u6709\u660e\u986f\u7684\u63d0\u5347\u3002",
"content": "<table><tr><td/><td/><td>INSERTION</td><td colspan=\"2\">DELETION SUBSTITUTION</td></tr><tr><td>Triphone sets (997)</td><td>68.07%</td><td>15.87%</td><td>4.43%</td><td>11.63%</td></tr><tr><td>ACL phone sets (288)</td><td>63.12%</td><td>19.73%</td><td>4.88%</td><td>12.32%</td></tr><tr><td>HAL phone sets (286)</td><td>64.23%</td><td>20.67%</td><td>4.75%</td><td>10.48%</td></tr><tr><td>FUN phone sets (260)</td><td>66.07%</td><td>16.94%</td><td>4.41%</td><td>12.71%</td></tr><tr><td colspan=\"5\">====================== English Across Taiwan, EAT ======================</td></tr><tr><td>\u7531\u5be6\u9a57\u7d50\u679c\u53ef\u77e5\uff0c\u5408\u4f75\u524d\u7684\u4e09\uf99a\u97f3\u7d20\u6a21\u578b(5. \u7d50\uf941\u53ca\u672a\uf92d\u5c55\u671b</td><td/><td/><td/><td/></tr><tr><td colspan=\"5\">\u672c\uf941\u6587\u63d0\u51fa\u61c9\u7528\u8072\u5b78\u76f8\u4f3c\ufa01\u53ca\u524d\u5f8c\u6587\u8108\u5206\u6790\u65bc\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u6709\u6548\u97f3\u7d20\u5b9a\u7fa9\uff0c\u4ee5 EAT \u4e2d\u82f1\u6587\u96d9\u8a9e\u8a9e</td></tr><tr><td colspan=\"5\">\uf9be\u70ba\uf9b5\u3002\u57fa\u65bc IPA \u6a19\u6e96\u5b9a\u7fa9\u4e4b\u591a\u8a9e\u55ae\u97f3\u7d20\u96c6\uff0c\u672c\u7814\u7a76\u8003\u616e\u4ee5\u767c\u97f3\u524d\u5f8c\u6587\u76f8\u4f9d\u4e09\uf99a\u97f3\u7d20\u6a21\u578b\u3002\u4ee5\u6b64\u5b9a\u7fa9\uff0c\u6211</td></tr><tr><td colspan=\"5\">\u5011\u5206\u5225\u4ee5\u8072\u5b78\u76f8\u4f3c\ufa01\u53ca\u524d\u5f8c\u6587\u8108\u5206\u6790\uff0c\u97f3\u7d20\u9593\u76f8\u4f3c\ufa01\u9ad8\u7684\u97f3\u7d20\u5408\u4f75\uff0c\u671f\u671b\u627e\u51fa\ufa1d\u7c21\u6709\u6548\u7684\u591a\u8a9e\u8a9e\u97f3\u8fa8\uf9fc\u97f3</td></tr><tr><td colspan=\"5\">\u7d20\u96c6\u3002\uf9dd\u7528\u97f3\u7d20 HMM \u6a21\u578b\uff0c\u4ee5\u76f4\u63a5\u6821\u6e96\u65b9\u6cd5\ufa00\u97f3\u4e26\u8a08\u7b97\u4e8b\u5f8c\u6a5f\uf961\u503c\uff0c\u5efa\uf9f7\u8072\u5b78\u76f8\u4f3c\ufa01\u77e9\u9663\u3002\uf9dd\u7528\u8a9e\u8a00\u8d85</td></tr><tr><td colspan=\"5\">\u7a7a\u9593\u76f8\u4f3c\ufa01\u5206\u6790(hyperspace analog to language, HAL)\uff0c\u627e\u51fa\u97f3\u7d20\u524d\u5f8c\u767c\u97f3\u7279\u6027\u6240\u9020\u6210\u7684\u8b8a\u97f3\u5f71\u97ff\uff0c\u5efa\uf9f7\u8a9e</td></tr><tr><td colspan=\"5\">\u8a00\u767c\u97f3\u4e0a\u76f8\u4f3c\ufa01\u77e9\u9663\u3002\u4e4b\u5f8c\uff0c\u4ee5\u8cc7\uf9be\u878d\u5408\u65b9\u6cd5\uff0c\u540c\u6642\u8003\u616e\u8072\u5b78\u548c\u8a9e\u8a00\u8d85\u7a7a\u9593\u76f8\u4f3c\ufa01\u77e9\u9663\u3002\uf9dd\u7528\u5411\uf97e\uf97e\u5316\u7fa4</td></tr><tr><td colspan=\"5\">\u96c6\u5206\u6790\uff0c\u627e\u51fa\u540c\u4e00\uf9d0\u5225\u4e4b\u97f3\u7d20\u5b9a\u7fa9\uff0c\u5efa\uf9f7\u6709\u6548\u800c\ufa1d\u7c21\u7684\u591a\u8a9e\u97f3\u7d20\u96c6\u3002\u5be6\u9a57\u8b49\u660e\uf9dd\u7528\u7d50\u5408\u8072\u5b78\u548c\u8a9e\u8a00\u8d85\u7a7a\u9593</td></tr><tr><td colspan=\"5\">\u76f8\u4f3c\ufa01\u77e9\u9663\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9054\u5230\uf97c\u597d\u7684\u591a\u8a9e\uf99a\u7e8c\u8a9e\u97f3\u8fa8\uf9fc\u7684\u6548\u679c\u3002\u672a\uf92d\u53ef\u4ee5\u5c07\u65b9\u6cd5\u61c9\u7528\u5728\u55ae\u4e00\u8a9e\u8a00\u8a9e\u97f3\u8fa8</td></tr></table>",
"num": null
}
}
}
} |