| { | |
| "paper_id": "O09-1019", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:11:18.925033Z" | |
| }, | |
| "title": "\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\uf9fc\u4e2d\u57fa\u65bc\u5c0f\u6ce2\u8f49\u63db\u4e4b\u5206\u983b\u7d71\u8a08\u88dc\u511f\u6280\u8853\u7684\u7814\u7a76 A Study of Sub-band Feature Statistics Compensation Techniques Based on a Discrete Wavelet Transform for Robust Speech Recognition", | |
| "authors": [ | |
| { | |
| "first": "Hao-Teng", | |
| "middle": [], | |
| "last": "\u8303\u9865\u9a30", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Fan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Wen-Hsiang", | |
| "middle": [], | |
| "last": "\u675c\u6587\u7965", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Tu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "The environ mental m ismatch caused by additiv e noise and/or channel distortion often degrades th e perform ance of a s peech reco gnition sys tem seriously. V arious ro bustness techniques have been proposed to reduce this mismatch, and one category of them aim s t o normalize the statistics of speech fea tures in bo th training and testing conditions. In general, these statistics norm alization methods deal with the sp eech feature sequ ences in a f ull-band manner, which som ewhat ignores the fact th at dif ferent m odulation frequency com ponents", | |
| "pdf_parse": { | |
| "paper_id": "O09-1019", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "The environ mental m ismatch caused by additiv e noise and/or channel distortion often degrades th e perform ance of a s peech reco gnition sys tem seriously. V arious ro bustness techniques have been proposed to reduce this mismatch, and one category of them aim s t o normalize the statistics of speech fea tures in bo th training and testing conditions. In general, these statistics norm alization methods deal with the sp eech feature sequ ences in a f ull-band manner, which som ewhat ignores the fact th at dif ferent m odulation frequency com ponents", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "have unequal importance for speech recognition.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "With the above observations, in this paper we propose that the speech feature streams be proce ssed in a sub-band ma nner. The processed temporal-domain feature sequence is first decomposed into non-uniform sub-bands us ing discrete wavelet transform (DWT), and then each sub-band stream is individuall y processed by the well-known normalization methods, like m ean and variance norm alization (MVN) and histogram equalization (HEQ) . Finally, we reconstruct the feature stream w ith all th e modif ied sub-band streams using inverse D WT. W ith this process, the com ponents that correspond to m ore important modulation spectral bands in the feature sequ ence can be processed separately . For the Aurora-2 clean-condition training task, the new proposed su b-band MVN and HEQ provide relative error rate reductions of 20.32% a nd 16.39% over the conventional MVN a nd HEQ, respectively. These results re veal that the proposed m ethods significantly enhance the robustness of speech features in noise-corrupted environments. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "( ) H H z \u8207 ( ) L H z \u8868\u793a\u70ba\u5206\u6790(analysis) \uf984\u6ce2\u5668\u4e4b\u9ad8\u901a\u8207\u4f4e\u901a\u7684\u8f49\u63db\u51fd\uf969(transfer function)\uff0c ( ) H G z \u8207 ( ) L G z \u5247\u70ba\u5408\u6210(synthesis)\uf984\u6ce2\u5668\u4e4b\u9ad8\u901a\u8207\u4f4e\u901a\u7684\u8f49\u63db\u51fd\uf969\uff0c\u4e14\u5b83\u5011 \u9808\u7b26\u5408\u4ee5\u4e0b\u7684\u689d\u4ef6\uff1a ( ) ( ) L H G z H z = , ( ) ( ) H L G z H z = \u2212 \u2212 (3-1) \u800c\u5728\u9ad8\u901a\u8207\u4f4e\u901a\u5206\u6790\uf984\u6ce2\u5668\u4e4b\u9593\u5b58\u6709\u4ee5\u4e0b\u95dc\u4fc2\uff1a ( ) ( ) H L H z H z = \u2212 \u5f0f(3-2) \u610f\u5373\u5176\u983b\uf961\u7279\u6027\u70ba ( ) ( ) ( ) j j H L H e H e \u03c9 \u03c0 \u03c9 \u2212 = \uff0c\u5176\u610f\u7fa9\u5728\u65bc\u9ad8\u901a\u8207\u4f4e\u901a\uf984\u6ce2\u5668\u4e4b\u983b\uf961\u97ff\u61c9\u6703 2 2 2 2 ( ) L H z ( ) H H z ( ) H G z ( ) L G z x[n] y[n] \u4ee5 2 \u03c0 \u03c9 = \u70ba\u4e2d\u5fc3\u5f62\u6210\u5de6\u53f3\u5c0d\u7a31\u7684\u5716\u5f62\uff0c\u5728\u5c0f\u6ce2\u8f49\u63db\u4e2d\uff0c\u5373\uf9dd\u7528\u6b64\u5f62\u5f0f\u7684\uf984\u6ce2\u5668\uf92d\u5c0d\u8a0a\u865f \u4f5c\u5206\u983b\u8655\uf9e4\u3002 \u5716\u56db\u6240\u8868\u793a\uf9ba\u4e00\u8a0a\u865f\u85c9\u7531\u4e0a\u8ff0\u4e4b\uf984\u6ce2\u5668\u8655\uf9e4\u7684\u5206\u89e3\u7a0b\u5e8f(decomposition process) \uff0c\u5373 \uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u7684\u5206\u983b\u8655\uf9e4\u3002\u5176\u4e2d\uff0c\u4e00\uf99a\uf905\u7684\uf978\u500d\u983b(octave-band)\u5206\u6790\uf984\u6ce2\u7d44\u8207\u4e4b\u5f8c\u7684\ufa09 \u4f4e\u53d6\u6a23(down-sampling)\u7684\u7d44\u5408\u901a\u5e38\u88ab\u7a31\u4f5c\u4e8c\u5143\u6a39(binary tree)\u7d50\u69cb\uff0c\u55ae\u4e00\u8f38\u5165\u5e8f\uf99c\u7d93\u7531\u5206 \u983b\u8655\uf9e4\u8207\ufa09\u4f4e\u53d6\u6a23\u5668(down-sampler)\u7684\u8f49\u63db\uff0c\u8f38\u51fa\u8b8a\u70ba\u5404\u5b50\u983b\u5e36\u5e8f\uf99c(sub-sequences)\u7684\u96c6 \u5408\u3002\u5728\u5716\u56db\u4e2d\uff0c\u6211\u5011\u770b\u5230\uf9ba\u4e00\u500b\u4e09\u968e(three-level)\u7684\u4e8c\u5143\u6a39\u5206\u6790\uf984\u6ce2\u5668\u7d44\u7d50\u69cb\uff0c\u5176\u4e2d\u9ad8\u901a ( ( ) H H z ) \u8207 \u4f4e \u901a ( ( ) L H z ) \uf984 \u6ce2 \u5668 \u90fd \u5177 \u6709 \u5b8c \u5168 \u91cd \u69cb (perfect reconstru ction) \u7684 \u96d9 \u901a \u9053 (two-channel)\u7279\u6027\uff0c\u5373\u8a0a\u865f\u901a\u904e\u6b64\uf978\uf984\u6ce2\u5668\u4e4b\u5f8c\uff0c\u4e26\u672a\u55aa\u5931\u4efb\u4f55\u8cc7\u8a0a\u6216\u5f15\u9032\u672a\u77e5\u7684\u5e72\u64fe \u8a0a\u865f\uff0c\u800c\u5f97\u4ee5\u5c07\u5206\u983b\u5f8c\u7684\u8a0a\u865f\u5b8c\u7f8e\u91cd\u5efa\u56de\u539f\u59cb\u8a0a\u865f\u3002\u53e6\u5916\uff0c\u5982\u679c\u8f38\u5165\u6b64\uf984\u6ce2\u5668\u7d44\u7684\u8a0a\u865f [ ] x n \u9577\ufa01\u70ba N \uff0c\u5728\u7b2c\u4e00\u968e\u9ad8\u901a\u5206\u6790\uf984\u6ce2\u5668\u4e4b\u8f38\u51fa [ ] 1 x n \u5373\u7d04\u70ba /2 N \uff0c\u800c\u518d\u4e0b\u4e00\u968e\u9ad8\u901a\u5206 \u6790\uf984\u6ce2\u5668\u8f38\u51fa [ ] 2 x n \u7d04\u70ba / 4 N \uff0c\u5982\u6b64\u91cd\u8907\u9019\u7a0b\u5e8f\uff0c\u5c31\u53ef\u4ee5\u5f97\u5230\u6240\u6709\u968e\u5c64\u4e4b\uf984\u6ce2\u5668\u7684\u8f38 \u51fa\u3002\u8868\u4e00\uf99c\u51fa\uf9ba\u5404\u5c64\uf984\u6ce2\u5668\u7684\u5176\u983b\u5e36\u7bc4\u570d\u53ca\u8f38\u51fa\u8a0a\u865f\u7684\u9577\ufa01\u3002\u4ee5\u4e0a\u6240\u8ff0\u4e4b\uf978\u500d\u983b(octave) \u5b8c\u5168\u91cd\u69cb QMF \uf984\u6ce2\u5668\u7d44\u5c0d\u8f38\u5165\u8a0a\u865f\u7684\u8655\uf9e4\u7a0b\u5e8f\uff0c\u5373\u70ba\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db(discrete wavelet transform, DWT)\uff0c\u7531\u4e0a\u8ff0\u53ef\u77e5\uff0c\u5982\u679c\u6240\u7528\u4e4b\uf984\u6ce2\u5668\u7d44\u7684\u968e\u5c64\uf969\u70ba L (\u76f8\u7576\u65bc L \u5c64\u7684\uf9ea\u6563 \u5c0f\u6ce2\u8f49\u63db) \uff0c\u5247\u8f38\u51fa\u7684\u7e3d\u5206\u983b\u8a0a\u865f\u7684\uf969\u76ee\u5247\u70ba 1 L + \u500b\u3002 \u5716\u56db \uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u7684\u5206\u89e3\u7a0b\u5e8f\u5716(\u968e\u5c64\uf969\u70ba 3) \u8868\u4e00\u3001\u4e09\u5c64\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db(DWT)\u6bcf\u4e00\u968e\u5c64\u7684\u8f38\u51fa\u8a0a\u865f\u9ede\uf969\u53ca\u76f8\u5c0d\u61c9\u7684\u983b\uf961\u7bc4\u570d ( [ ] x n \u53d6\u6a23\u983b\uf961\u70ba s F Hz ) \u8a0a\u865f \u7e3d\u9ede\uf969 \u983b\uf961\u7bc4\u570d [ ] x n N 0, 2 Hz s F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 [ ] 1 x n 2 N 4 Hz, 2 Hz s s F F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 [ ] 2 x n 4 N 8 Hz, 4 Hz s s F F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 [ ] 3 x n 8 N 16 Hz, 8 Hz s s F F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 [ ] 4 x n 8 N 0, 16 Hz s F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 \u5f9e\u4e0a\u8868\u4e00\u53ef\u77e5\uff0c\u5982\u679c\u5e8f\uf99c [ ] x n \u6db5\u84cb\u7684\u983b\uf961\u7bc4\u570d\u70ba[0, 2 s F Hz] \uff0c\u5176\u4e2d s F \u70ba [ ] x n \u7684\u53d6 \u6a23 \u983b \uf961 \uff0c \u5247 \u7d93 \u7531 \u7b2c \u4e00 \u968e \u6b63 \u4ea4 \u93e1 \u50cf \uf984 \u6ce2 \u5668 \u7d44 \u4e4b \u9ad8 \u983b \u8f38 \u51fa [ ] 1 x n \uff0c \u983b \uf961 \u7bc4 \u570d \u70ba 4 Hz, 2 Hz s s F F \u23a1 \u23a4 \u23a2 \u23a5 \u23a3 \u23a6 \uff0c\u4f9d\u6b64\uf9d0\u63a8\uff0c\u9010\u6b65\u5f80\u4f4e\u983b\uf961\u90e8\u4efd\u505a\uf967\u7b49\u5206\ufa00\u5272\uff0c\u96a8\u8457\u983b\uf961\u8d8a\u9ad8\uff0c\u5176 L H 2 \u2193 2 \u2193 H H L H 2 \u2193 2 \u2193 H H L H 2 \u2193 2 \u2193 [ ] x n [ ] 1 x n [ ] 2 x n [ ] 3 x n [ ] 4 x n H H Level 1 Level 3 Level 2 \u983b\u5bec\u5247\u8d8a\u5927\u3002\u7531\u4e0a\u6240\u8ff0\uff0c\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u7684\u7b2ck \u500b\u8f38\u51fa [ ] k x n \uff0c\u76f8\u7576\u65bc\u662f\u539f\u59cb\u5e8f\uf99c [ ] x n \u8207 \u7b2ck \u500b\u5e36\u901a\uf984\u6ce2\u5668\u4e4b\u8108\u885d\u97ff\u61c9(impulse response)\u76f8\u4e92\u647a\u7a4d(convolution)\u7684\u7d50\u679c\uff0c\u5982\u5f0f(3-3) \u6240\u793a\uff1a [ ] [ ] [ ] 1 ,1 2 , 0 -1 , 2 , . k k m k k k m h n m x m k L x n h n m x m k L \u221e + =\u2212\u221e \u221e =\u2212\u221e \u23a7 \u23aa \u23a1 \u23a4 \u23aa \u2212 \u2264 \u2264 \u23aa \u23a3 \u23a6 \u23aa \u23aa = \u23a8 \u23aa \u23aa \u23a1 \u23a4 \u2212 = \u23aa \u23a3 \u23a6 \u23aa \u23aa \u23a9 \u2211 \u2211 \u5f0f(3-3) \u5176\u4e2d 1 ,1 2 k k h n + \u23a1 \u23a4 \u23a3 \u23a6 \u8207 2 k k h n \u23a1 \u23a4 \u23a3 \u23a6 \u70ba\u539f\u59cb\u8108\u885d\u97ff\u61c9 [ ] ,1 k h n \u8207 [ ] k h n \ufa09\u4f4e\u53d6\u6a23\u800c\u5f97\uff0c\u800c\u9ad8\u901a\uf984\u6ce2\u5668\u4e4b \u8f38\u51fa\uff0c\u7a31\u70ba\u7d30\u7bc0(detail)\u4fc2\uf969\uff1b\u4f4e\u901a\uf984\u6ce2\u5668\u4e4b\u8f38\u51fa\u5247\u7a31\u70ba\u8fd1\u4f3c(approximation)\u4fc2\uf969\u3002 \uf974 \u8981 \u85c9 \u7531 \u6240 \u6709 \u5b50 \u983b \u5e36 \u8a0a \u865f \u7684 \u96c6 \u5408 \u5f97 \u5230 \u539f \u59cb \u5e8f \uf99c [ ] x n \uff0c \u5176 \u904e \u7a0b \u7a31 \u70ba \u91cd \u5efa \u7a0b \u5e8f (reconstruction process)\uff0c\u6b64\u6070\u70ba\u524d\u8ff0\u4e4b\u5206\u89e3\u7a0b\u5e8f\u7684\u53cd\u7a0b\u5e8f(inverse process)\uff0c\u5373\u4f7f\u7528\u6240\u5f97 \u4e4b [ ] { } k x n \u7d93 L \u968e\uf978\u500d\u983b\u5b8c\u5168\u91cd\u69cb QMF \u5408\u6210\uf984\u6ce2\u5668\u7d44\u9010\u5c64\u8655\uf9e4\uff0c\u6b64\u904e\u7a0b\u5373\u70ba\u53cd\uf9ea\u6563\u5c0f\u6ce2 \u8f49\u63db(inverse discrete wavelet transform, IDWT)\uff0c\u5982\u4e0b\u5716\u4e94\u6240\u793a\uff1a \u5716\u4e94 \u53cd\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u7684\u91cd\u5efa\u7a0b\u5e8f\u5716(\u968e\u5c64\uf969\u70ba 3) \u9084\u539f\u7a0b\u5e8f\u5176\uf969\u5b78\u5f0f\u5982\u5f0f(3-4)\uff1a [ ] [ ] [ ] 1 1 ,1 0 2 2 L k L k k k L k m m x n g n m x m g n m x m \u2212 \u221e \u221e + = =\u2212\u221e =\u2212\u221e \u23a1 \u23a4 \u23a1 \u23a4 = \u2212 + \u2212 \u23a3 \u23a6 \u23a3 \u23a6 \u2211 \u2211 \u2211 , (3-4) \u5176\u4e2d 1 ,1 2 k k g n + \u23a1 \u23a4 \u23a3 \u23a6 \u8207 2 k k g n \u23a1 \u23a4 \u23a3 \u23a6 \u5206\u5225\u70ba\u539f\u59cb\u8108\u885d\u97ff\u61c9 [ ] ,1 k g n \u8207 [ ] k g n \u63d0\u5347\u53d6\u6a23\u800c\u5f97\u3002\u5716\u4e94\u4e4b\u9084\u539f \u7a0b\u5e8f\uff0c\u5373\u662f\u5c07\u5404\u5b50\u983b\u5e36\u7684\u8a0a\u865f\u4ee5\u63d0\u5347\u53d6\u6a23(up-sampling)\u7684\u65b9\u5f0f\u589e\u52a0\u5e8f\uf99c\u9ede\uf969\uff0c\u518d\u7d93\u904e\u9ad8 \u901a( ( ) ( ) H H G z H z = )\u8207\u4f4e\u901a( ( ) ( ) L L G z H z = )\u4e4b\u5408\u6210\uf984\u6ce2\u5668\u8655\uf9e4\uff0c\u5982\u679c\u7b2c\u4e09\u968e\u8f38\u5165\u8a0a\u865f\u9ede \uf969\u70ba 8 N \uff0c\u5247\u5728\u7b2c\u4e09\u968e\u8f38\u51fa\u8a0a\u865f\u9ede\uf969\u7d04\u70ba 4 N \uff0c\u800c\u7b2c\u4e8c\u968e\u8f38\u51fa\u8a0a\u865f\u9ede\uf969\u7d04\u70ba 2 N \uff0c\u5982\u6b64 \u91cd\u8986\u6b64\u7a0b\u5e8f\uff0c\u5247\u6700\u5f8c\u6240\u5f97\u4e4b\u8a0a\u865f\u70ba\u539f\u59cb N \u9ede\u4e4b\u8a0a\u865f [ ] x n : \u4ee5\u4e0a\u6240\u8ff0\u70ba\u5c0f\u6ce2\u8f49\u63db\u4e4b\u5206\u6790(analysis)\u8207\u5408\u6210(synthesis)\u7a0b\u5e8f\uff0c\u7d93\u7531\u6b64\u8f49\u63db\u5f8c\uff0c\u8a0a\u865f \u88ab\u5206\u89e3\u6210\u5404\u500b\u5b50\u983b\u5e36\u4e4b\u8a0a\u865f\uff0c\u5982\u8868\u4e00\u6240\u793a\uff0c\u4f4e\u983b\u90e8\u5206\u7684\u5b50\u983b\u5e36\u983b\u5bec\u8f03\u5c0f\uff0c\u800c\u9ad8\u983b\u90e8\u5206\u7684 \u5b50\u983b\u5e36\u983b\u5bec\u8f03\u5927\u3002\u85c9\u7531\u4ee5\u4e0a\u6240\u8ff0\u7684\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u7a0b\u5e8f\uff0c\u6211\u5011\u53ef\u4ee5\u5c07\u8a9e\u97f3\u7279\u5fb5\u6642\u9593\u5e8f\uf99c\u4f5c \u5206\u983b\u7684\u8655\uf9e4\uff0c\u9032\u800c\u91dd\u5c0d\uf967\u540c\u8abf\u8b8a\u983b\u5e36\u6210\u5206\u7684\u8a9e\u97f3\u7279\u5fb5\u5e8f\uf99c\u5206\u5225\u4f5c\u8655\uf9e4\uff0c\u5728\u4e0b\u4e00\u7ae0\uf9e8\uff0c\u6211 \u5011\u5c07\u4ecb\u7d39\u5176\u5c0d\u61c9\u7684\u7684\u5206\u983b\u5f0f\u7279\u5fb5\u7d71\u8a08\u88dc\u511f\u6cd5\u3002 \u56db\u3001\u5206\u983b\u5e36\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5 \u5728\u9019\u4e00\u7ae0\u4e2d\uff0c\u6211\u5011\u9996\u5148\u5728\u7b2c\u4e00\u7bc0\u4ecb\u7d39\u6240\u65b0\u63d0\u51fa\u4e4b\u5206\u983b\u5e36\u7279\u5fb5\u7d71\u8a08\u88dc\u511f\u6cd5\u7684\u6b65\u9a5f\u53ca\u7279 \u6027\uff0c\u63a5\u8457\u5728\u7b2c\u4e8c\u7bc0\u4e2d\uff0c\u6211\u5011\u5c07\u4ee5\u4e00\u8a9e\uf906\u70ba\uf9b5\uff0c\u9a57\u8b49\u6240\u63d0\u4e4b\u65b0\u65b9\u6cd5\u8db3\u4ee5\u6709\u6548\ufa09\u4f4e\u96dc\u8a0a\u5c0d\u8a9e \u97f3\u8abf\u8b8a\u983b\u8b5c\u4e4b\u5e72\u64fe\u3002 Level 3 [ ] 4 x n [ ] 3 x n 2 \u2191 2 \u2191 L G H G \u2295 2 \u2191 L G 2 \u2191 H G [ ] 2 x n \u2295 2 \u2191 L G 2 \u2191 H G [ ] 1 x n \u2295 [ ] x n Level 2 Level 1 (\u4e00) \u5206\u983b\u5e36\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u7684\u6b65\u9a5f\uf96f\u660e \u5047\u8a2d\u4e00\u6bb5\u8a9e\uf906(utterance)\u7684\u67d0\u4e00\u7dad\u6885\u723e\u5012\u983b\u8b5c\u8a9e\u97f3\u7279\u5fb5\u4ee5\u4e0b\u5f0f(4-1)\u8868\u793a: ( ) [ ] { } ;1 , 0 1 , m x n n N m M < \u2264 \u2264 \u2264 \u2212 (4-1) \u5176\u4e2d N \u70ba\u6b64\u7279\u5fb5\u5e8f\uf99c\u7684\u7e3d\u97f3\u6846\uf969\uff0c M \u8868\u793a\u6bcf\u4e00\u97f3\u6846\u4e2d\u7684\u7279\u5fb5\u7e3d\uf969\u3002\u6b64\u7279\u5fb5\u5e8f\uf99c\u76f8\u7576\u65bc \u6db5\u84cb\uf9ba\u5168\u8abf\u8b8a\u983b\u5e36(full-band)\u7684\u8a9e\u97f3\u8cc7\u8a0a\uff0c\u7136\u800c\uff0c\u7531\u524d\u9762\u7ae0\u7bc0\u6240\u8ff0\uff0c\uf967\u540c\u7684\u983b\u5e36\u6210\u4efd\uff0c \u5c0d\u65bc\u8a9e\u97f3\u8fa8\u8a8d\u7684\u91cd\u8981\u6027\u6709\u6240\uf967\u540c\uff0c\u57fa\u65bc\u6b64\u9805\uf9e4\u7531\uff0c\u9019\uf9e8\u6211\u5011\u4f7f\u7528\u5206\u983b\u7684\u6280\u8853\uff0c\u5c07\u6b64\u7279\u5fb5 \u5e8f\uf99c\u5206\u89e3\u6210\u5404\uf967\u540c\u983b\uf961\u7684\u6210\u5206\uff0c\u5982\u4ee5\u4e0b\u6b65\u9a5f (\u70ba\uf9ba\u7c21\uf9e0\uf96f\u660e\u8d77\ufa0a\uff0c\u6211\u5011\u5728\u4e4b\u5f8c\u7684\u8a0e\uf941\u4e2d\uff0c \u5c07\uf96d\uf976\u5f0f(4-1)\u4e2d\u4ee3\u8868\uf967\u540c\u7dad\u7279\u5fb5\u7684\u4e0a\u6a19\" ( ) m \"\uff0c\u56e0\u70ba\u6211\u5011\u662f\u5c0d\u6bcf\u4e00\u500b\uf967\u540c\u7dad\u7684\u7279\u5fb5\u5e8f\uf99c \u7686\u4f5c\u540c\u6a23\u8655\uf9e4)\uff1a \u9996\u5148\uff0c\u6211\u5011\u5c07\u539f\u59cb\u7279\u5fb5\u5e8f\uf99c [ ] { } x n \ufa00\u5272\u6210 L \u500b\u5206\u983b\u5e36\u4e14\u5047\u8a2d\u6bcf\u4e00\u5206\u983b\u5e36\u90fd\u70ba\u5404\u81ea\u7368 \uf9f7\uff0c\u800c\u6bcf\u4e00\u983b\u5e36\u4e2d\u7684\u5e8f\uf99c\u8868\u793a\u70ba [ ] { },1 x n L \u2264 \u2264 \uff0c\u6b64\ufa00\u5272\u983b\u5e36\u7684\u65b9\u6cd5\u662f\u5c07\u539f\u59cb\u7279\u5fb5 \u901a\u904e\u4e00\u500d\u983b(octave-band)\u5e36\u901a\uf984\u6ce2\u5668\u7d44\uff0c\u6bcf\u4e00\u5b50\u983b\u5e36\u8a0a\u865f\u518d\u4f5c\ufa09\u4f4e\u53d6\u6a23(down-sampling) \u8655\uf9e4\uff0c\u6b64\u6b65\u9a5f\u7b49\u6548\u65bc\u57f7\ufa08( ) 1 L \u2212 \u968e\u7684\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db(discrete wavelet transform, DWT) \u65bc \u7279\u5fb5\u5e8f\uf99c [ ] x n \u4e0a\u3002\u53e6\u5916\uff0c\u5047\u8a2d\u7279\u5fb5\u5e8f\uf99c [ ] { } x n \u97f3\u6846\u53d6\u6a23\uf961\u70ba s F (Hz) \uff0c\u5247\u5176\u8abf\u8b8a\u983b\u8b5c\u983b \uf961\u7bc4\u570d\u70ba[ ] 0, / 2 s F \uff0c\u56e0\u6b64\uff0c\u7b2c \u500b\u5206\u983b\u5e36\u5e8f\uf99c\u7684\u983b\uf961\u7bc4\u570d\uff0c\u53ef\u88ab\u8fd1\u4f3c\u8868\u793a\u6210\u5f0f(4-2)\uff1a ( ) ( ) ( ) 1 2 1 1 1 1 0, / 2 if =1 2 2 2 / 2 , / 2 if 2, 3, , 2 2 s L s s L L F F F L \u2212 \u2212 \u2212 \u2212 \u2212 \u23a7\u23a1 \u23a4 \u23aa \u23aa \u23a2 \u23a5 \u23aa \u23a2 \u23a5 \u23aa\u23a3 \u23a6 \u23aa \u23a8 \u23a1 \u23a4 \u23aa \u23aa \u23a2 \u23a5 = \u23aa \u23aa\u23a2 \u23a5 \u23aa\u23a3 \u23a6 \u23a9 (4-2) \u5728 DWT \u7a0b\u5e8f\u4e2d\uff0c\u5176\u65b9\u5f0f\u662f\u5c07\u4e00\u4e3b\u983b\u5e36\u4f9d\u983b\u5bec\u5148\u7b49\ufa00\u70ba\uf978\u500b\u526f\u983b\u5e36\uff0c\u7136\u5f8c\u4fdd\u6301\u9ad8\u983b \u5e36\uf967\u52d5\uff0c\u5c07\u4f4e\u983b\u5e36\u518d\u7b49\ufa00\u6210\uf978\u500b\u526f\u983b\u5e36\uff0c\u5982\u6b64\u53cd\u8986\u9032\ufa08\uff0c\u56e0\u6b64\u76f8\u7576\u65bc\u4f4e\u983b\u90e8\u4efd\u6703\u4f7f\u7528\u8f03 \u591a\u500b\u983b\u5bec\u8f03\u5c0f\u7684\uf984\u6ce2\u5668\uff0c\u800c\u9ad8\u983b\u90e8\u4efd\u5247\u7528\u8f03\u5c11\u500b\u983b\u5bec\u8f03\u5927\u7684\uf984\u6ce2\u5668\uff0c\u800c\u56e0\u70ba DWT \u7a0b\u5e8f \u4e2d\u7684\ufa09\u4f4e\u53d6\u6a23(down-sampling)\u7684\u904b\u7b97\uff0c\u6240\u4ee5\u6bcf\u4e00\u5206\u983b\u5e36\u7684\u5e8f\uf99c [ ] { } x n \u9577\ufa01\u7d04\u6b63\u6bd4\u65bc\u983b \u5bec\u7684\u5927\u5c0f\u3002 \u63a5\u8457\uff0c\u5c07\u4e0a\u6b65\u9a5f\u6240\u5f97\u7684\u5206\u983b\u5e36\u5e8f\uf99c [ ] { } x n \u505a\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\uff0c\u5f97\u5230\u65b0\u7684\u5206\u983b\u5e36\u5e8f \uf99c\uff0c\u8868\u793a\u70ba [ ] { } x n \uff0c\u5176\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u7684\u65b9\u5f0f\u662f\u5c07\u6bcf\u4e00\u8a9e\uf906\u4e4b\u5b50\u983b\u5e36\u7279\u5fb5 [ ] { } x n \u7684\u7d71 \u8a08\uf97e\uff0c\u8b6c\u5982\u5e73\u5747\u503c(mean)\u3001\u8b8a\uf962\uf969(variance)\u6216\u662f\uf901\u9ad8\u968e\u7684\u52d5\u5dee(moments)\u4f5c\u8655\uf9e4\uff0c\u4f7f\u65b0 \u7684\u7279\u5fb5\uf96b\uf969 [ ] { } x n \u7684\u7d71\u8a08\uf97e\u7b49\u540c\u6216\u903c\u8fd1\u4e00\u76ee\u6a19(target)\u7d71\u8a08\uf97e\uff0c\u800c\u6b64\u76ee\u6a19\u7d71\u8a08\uf97e\u662f\u7531\u4e7e \u6de8\u8a13\uf996\u8a9e\uf9be\u5eab\u4e2d\uff0c\u6240\u6709\u8a9e\uf906\u4e4b\u5b50\u983b\u5e36\u7279\u5fb5 [ ] { } x n \u4f30\u6e2c\u8a08\u7b97\u800c\u5f97\u3002\u5728\u9019\uf9e8\u6211\u5011\u4f7f\u7528\u7684\u7279 \u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u6709\uf978\u7a2e\uff0c\u5206\u5225\u70ba\u5012\u983b\u8b5c\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u6b63\u898f\u5316\u6cd5(MVN)\u8207\u7d71\u8a08\u5716\u7b49\u5316\u6cd5 (HEQ)\uff0c\u4ee5 MVN \u6cd5\u800c\u8a00\uff0c\u6240\u5f97\u65b0\u7684\u5206\u983b\u5e36\u5e8f\uf99c [ ] { } c n \u53ef\u8868\u793a\u70ba\u4e0b\u5f0f(4-3)\uff1a [ ] [ ] , , , , s t t s x n x n \u03bc \u03c3 \u03bc \u03c3 \u239b \u239e \u2212 \u239f \u239c \u239f \u239c = \u00d7 + \u239f \u239c \u239f \u239f \u239c \u239d \u23a0 (4-3) \u5176\u4e2d ,s \u03bc \u8207 2 ,s \u03c3 \u5206\u5225\u70ba\u76ee\u524d\u8655\uf9e4\u7684\u55ae\u4e00(single)\u5206\u983b\u5e36\u5e8f\uf99c [ ] { } x n \u7684\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\uff0c\u800c ,t \u03bc \u8207 2 ,t \u03c3 \u70ba\u76ee\u6a19(target)\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\uff0c\u6b64\u76ee\u6a19\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u662f\u7531\u539f\u59cb\u4e7e\u6de8\u8a13\uf996\u8a9e\uf9be \u5eab\u4e2d\u6240\u6709\u5206\u983b\u5e36\u7279\u5fb5\u5e8f\uf99c [ ] { } x n \u4f30\u6e2c\u800c\u5f97\u3002\u540c\u6a23\u5730\uff0c\u5982\u4ee5 HEQ \u4f5c\u70ba\u7d71\u8a08\u88dc\u511f\u6cd5\uff0c\u5247 [ ] { } x n \u8207 [ ] { } x n \u5f7c\u6b64\u95dc\u4fc2\u70ba\u4e0b\u5f0f(4-4)\uff1a [ ] [ ] ( ) ( ) 1 , , X t X s x n F F x n \u2212 = (4-4) \u5176\u4e2d ( ) , . X s F \u70ba\u76ee\u524d\u8655\uf9e4\u7684\u55ae\u4e00\u5206\u983b\u5e36\u5e8f\uf99c [ ] { } x n \u6240\u4f30\u6e2c\u7684\u6a5f\uf961\u5206\u4f48\u51fd\uf969(probability distribution function)\uff0c\u800c ( ) , . X t F \u662f\u7531\u539f\u59cb\u4e7e\u6de8\u8a13\uf996\u8a9e\u5eab\u4e2d\u6240\u6709\u5206\u983b\u5e36\u7279\u5fb5\u5e8f\uf99c [ ] { } x n \u6240 \u4f30\u6e2c\u800c\u5f97\u7684\u6a5f\uf961\u5206\u4f48\u51fd\uf969\u3002 \u6700\u5f8c\uff0c\u5c07\u6240\u6709\u7684\u5206\u983b\u5e36\u5e8f\uf99c [ ] { } x n (\u5305\u542b\uf9ba\uf901\u65b0\u904e\u5f8c\u8207\u672a\uf901\u65b0\u7684\u5206\u983b\u5e36\u5e8f\uf99c)\u900f\u904e ( ) 1 L \u2212 \u968e\u53cd\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db(inverse discrete wavelet transform, IDWT)\uff0c\u91cd\u5efa\u70ba\u65b0\u7684\u7279\u5fb5\u6642 \u9593\u5e8f\uf99c\uff0c\u6b64\u5373\u70ba\u6211\u5011\u6700\u5f8c\u4f7f\u7528\u4e4b\u8a9e\u97f3\u7279\u5fb5\u5e8f\uf99c [ ] { } x n \u3002 \u4e0a\u8ff0\u5206\u983b\u5e36\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u7684\uf9ca\u7a0b\u5716\u7e6a\u65bc\u4e0b\u5716\uf9d1\uff1a x[n] H L (z) H H (z) \u21932 \u21932 H H (z) \u21932 \u21932 H L (z) H H (z) \u21932 S S S S \u21932 \u21912 \u21912 H L (z) G L (z) G H (z) \u21912 \u21912 G L (z) G H (z) \u21912 \u21912 G L (z) G H (z) [ ] x n [ ] 1 x n [ ] 2 x n [ ] 3 x n [ ] 4 x n [ ] 1 x n [ ] 2 x n [ ] 3 x n [ ]", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
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| }, | |
| "TABREF0": { | |
| "content": "<table><tr><td>\uf961(zero-crossing rate)\uf92d\u6c7a\u5b9a\u542b\u6709\u8a9e\u97f3\u6210\u5206\u7684\u4f4d\u7f6e\uff1b\u5728\u983b\u57df(frequency domain)\u4e0a\uff0c\u5247\u901a\u5e38 (cepstral mean subtraction, CMS)[8] \u3001\u5012\u983b\u8b5c\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u6b63\u898f\u5316\u6cd5(cepstral mean and</td></tr><tr><td>\u662f\u8a08\u7b97\u8a9e\u97f3\u983b\u8b5c\u7684\u71b5(entropy)\uf92d\u7372\u5f97\u8a9e\u97f3\u6210\u5206\u7684\u8cc7\u8a0a[3]\u3002\u800c\u5c0f\u6ce2\u5728\u6b64\u65b9\u5411\u4e0a\u6240\u63d0\u51fa\u7684\u6280 variance normalization, MVN)[9]\u3001\u5012\u983b\u8b5c\u5e73\u5747\u8207\u8b8a\uf962\uf969\u6b63\u898f\u5316\u6cd5\u7d50\u5408\u81ea\u52d5\u56de\u6b78\u52d5\u614b\u5e73\u5747</td></tr><tr><td>\u8853\u76f8\u5c0d\u8f03\u591a\uff0c\u8b6c\u5982\u5728\u6587\u737b[4]\u4e2d\u63d0\u5230\uf9ba\u4f7f\u7528\u5c0f\u6ce2\u8f49\u63db\u7684\u4fc2\uf969\u80fd\uf97e\u6bd4\uf9b5\u5224\u5b9a\u8a9e\u97f3\u53ca\u975e\u8a9e\u97f3 \uf984\u6ce2\u5668\u6cd5(cepstral m ean and variance norm alization p lus auto-regress ive m oving average</td></tr><tr><td>(non-speech)\u6210\u5206\uff0c\u6216\u662f\u5728\u53e6\u4e00[5]\u6587\u737b\uf9e8\u63d0\u51fa\u8a08\u7b97\u5c0f\u6ce2\u4fc2\uf969\u4e4b\u8b8a\uf962\uf969\uff0c\u5c07\u5176\u8996\u70ba\u4e00\u7d44\u96a8 filtering, MVA)[10]\u8207\u7d71\u8a08\u5716\u6b63\u898f\u5316\u6cd5(histogram equalization, HEQ)[11]\u7b49\u3002</td></tr><tr><td>\u6a5f\u8b8a\uf969(random variable) \u7d93\u7531\u6a5f\uf961\uf9e4\uf941\u4e4b\u7d50\u679c\u5224\u5b9a\uff0c\u6240\u5f97\u5206\uf9d0\u65b9\u6cd5\u76f8\u8f03\u65bc\u4e4b\u524d\u65b9\u5f0f\u80fd\uf901 \u4e0a\u8ff0\u5404\u7a2e\u7684\u6b63\u898f\u5316\u6280\u8853\u4e2d\uff0c\u7686\u662f\u628a\u55ae\u4e00\u7dad\u7279\u5fb5\u5e8f\uf99c\u4e4b\u6240\u6709\u7279\u5fb5\u8996\u70ba\u540c\u4e00\u500b\u96a8\u6a5f\u8b8a\uf969</td></tr><tr><td>\u7cbe\u78ba\u5224\u5225\u51fa\u8a9e\u97f3\u8ddf\u975e\u8a9e\u97f3\u4e4b\u6210\u4efd\u3002 \u7684\u53d6\u6a23(sample)\uff0c\u9032\u800c\u76f4\u63a5\u4f30\u6e2c\u6b64\u96a8\u6a5f\u8b8a\uf969\u4e4b\u7d71\u8a08\uf96b\uf969\uff0c\u8b6c\u5982\u671f\u671b\u503c(mean)\u3001\u8b8a\uf962\uf969</td></tr><tr><td>(\u4e09) \u5f37\u5065\u6027\u8a9e\u97f3\u7279\u5fb5\u64f7\u53d6(robust speech feature extraction) (variance)\u8207\u6a5f\uf961\u5206\u4f48(probability distribution)\u7b49\u3002\u96d6\u7136\u7a0b\u5e8f\u4e0a\uf9e0\u65bc\u5be6\u73fe\uff0c\u537b\u76f8\u5c0d\u5ffd\uf976\uf9ba\u4e00</td></tr><tr><td>\u6b64\uf9d0\u7684\u8a9e\u97f3\u8655\uf9e4\u6280\u8853\u65b9\u6cd5\u76ee\u7684\u662f\u64f7\u53d6\uf967\u5bb9\uf9e0\u53d7\u5230\u96dc\u8a0a\u5e72\u64fe\u7684\u8a9e\u97f3\u7279\u5fb5\uf96b\uf969\uff0c\u50b3\u7d71 \u6bb5\u8a9e\uf906\u4e4b\u4e2d\uff0c\u5176\u7279\u5fb5\u96a8\u6642\u9593\u8b8a\u5316\u7684\u7279\u6027\uff0c\uf9b5\u5982\u8abf\u8b8a\u983b\u8b5c\u7684\u8cc7\u8a0a\u3002\u5f9e\u53e6\u4e00\u89c0\u9ede\uf92d\u770b\uff0c\u9019\u4e9b</td></tr><tr><td>\u7684\u5f37\u5065\u6027\u8a9e\u97f3\u7279\u5fb5\u64f7\u53d6\u6280\u8853\u5927\u591a\uf969\u662f\u5728\u63a2\u8a0e\u8a9e\u97f3\u7279\u5fb5\u7684\u983b\u8b5c\u6027\u8cea\u9032\u800c\u767c\u5c55\u800c\u5f97\uff0c\u63db\uf906\u8a71 \u4f5c\u6cd5\u7b49\u540c\u65bc\u5c07\u5168\u90e8\u8abf\u8b8a\u983b\uf961\u4e4b\u6210\u4efd\u4e00\u4f75\u505a\u8655\uf9e4\u3002\u7136\u800c\u6839\u64da\u904e\u53bb\u8a31\u591a\u7684\u7814\u7a76\u767c\u73fe\uff0c\uf967\u540c\u7684</td></tr><tr><td>\uf96f\uff0c\u5176\u6240\u4f7f\u7528\u7684\u8f49\u63db\u6cd5\u70ba\u6709\u540d\u7684\u5085\uf9f7\uf96e\u8f49\u63db(Fourier transform)\u3002\u7136\u800c\u5c0f\u6ce2\u8655\uf9e4\u4e5f\u76f8\u7e7c\u61c9 \u8abf\u8b8a\u983b\u8b5c\u6210\u4efd\u5c0d\u65bc\u8a9e\u97f3\u8fa8\uf9fc\u64c1\u6709\uf967\u540c\u7684\u91cd\u8981\u6027\uff0c\uf901\u7cbe\u78ba\u5730\uf96f\uff0c\u5728 N.Kanedera \u5b78\u8005[12]</td></tr><tr><td>\u7528\u65bc\u5f37\u5065\u6027\u8a9e\u97f3\u7279\u5fb5\u64f7\u53d6\u6280\u8853\u4e0a\uff0c\uf9b5\u5982\uff0c\u5728[6]\u63d0\u51fa\u5c07\u539f\u59cb\u6885\u723e\u5012\u983b\u8b5c\u7279\u5fb5(mel-frequency \u8a73\u7d30\u6307\u51fa\u5927\u90e8\u5206\u7684\u8a9e\u97f3\u8fa8\uf9fc\u8cc7\u8a0a\u5206\u5e03\u5728 1 Hz \u548c 16 Hz \u7684\u8abf\u8b8a\u983b\uf961\u4e4b\u9593\uff0c\u4e14\u4e3b\u8981\u96c6\u4e2d\u5728 4</td></tr><tr><td>cepstral co efficients, M FCC)\u4e2d\u7684\uf9ea\u6563\u9918\u5f26\u8f49\u63db(discrete cosine trans form, DCT) \u7a0b\u5e8f\u6539\u8b8a Hz \u9644\u8fd1\u3002\u56e0\u6b64\uff0c\u8a31\u591a\u77e5\u540d\u4e14\u6210\u529f\u7684\u6642\u9593\u5e8f\uf99c\uf984\u6ce2\u5668(temporal filters)[13,14] \uff0c\u90fd\u662f\u7279\u5225</td></tr><tr><td>\u70ba\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db(discrete wavelet transform, DWT)\uff0c\u5176\uf941\u6587\u5448\u73fe\u7684\u5be6\u9a57\u7d50\u679c\u986f\u793a\u6240\u5f97\u5230\u7684 \u5f37\u8abf\u51fa\u9019\u4e9b\u91cd\u8981\u7684\u8abf\u8b8a\u983b\uf961\u6210\u5206\uff0c\u9032\u800c\u986f\u793a\u80fd\u6709\u6548\u6539\u5584\u96dc\u8a0a\u74b0\u5883\u4e0b\u8a9e\u97f3\u8fa8\uf9fc\u7684\u6548\u80fd\u3002</td></tr><tr><td>\u7279\u5fb5\u6bd4\u539f\u59cb MFCC \uf901\u5177\u6709\u96dc\u8a0a\u74b0\u5883\u4e4b\u5f37\u5065\u6027\u3002 \u800c\u524d\u9762\u4ecb\u7d39\u7684\u5404\u7a2e\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6f14\u7b97\u6cd5\uff0c\u53ef\u80fd\u7f3a\u5931\u5728\u65bc\u7121\u6cd5\u6709\u6548\u7a81\u986f\uf967\u540c\u8abf\u8b8a\u983b</td></tr><tr><td>\u4e00\u3001\u7dd2\uf941 \u8fd1\uf98e\uf92d\uff0c\u8a9e\u97f3\u8655\uf9e4\u4e4b\uf9b4\u57df\u7684\u5b78\u8005\u6301\u7e8c\u5730\u958b\u767c\u7814\u7a76\uff0c\u4f7f\u8a9e\u97f3\u8655\uf9e4\u76f8\u95dc\uf9e4\uf941\u8207\u6280\u8853\uf967 \u65b7\u7cbe\u9032\u6210\u719f\uff0c\u9010\u6f38\u8da8\u65bc\u5be6\u969b\u61c9\u7528\u7684\u76ee\u7684\uff0c\u5c31\u8a9e\u97f3\u8fa8\uf9fc(speech recogn ition)\u800c\u8a00\uff0c\u5176\u7cfb\u7d71 \u5e38\u56e0\u6240\u5728\u74b0\u5883\u4e4b\u96dc\u8a0a\u5e72\u64fe\u6216\u662f\u50b3\u8f38\u901a\u9053\u7684\u6548\u61c9\uff0c\u800c\u4f7f\u8fa8\uf9fc\u6548\u80fd\u53d7\u5230\u660e\u986f\u5f71\u97ff\u3002\u91dd\u5c0d\u9019\u6a23 \u7684\u554f\u984c\uff0c\u8fd1\uf98e\uf92d\u7684\u7814\u7a76\u5b78\u8005\u63d0\u51fa\uf9ba\u4e00\u7cfb\uf99c\u7684\u74b0\u5883\u5f37\u5065\u6027(environmental robustness)\u6280\u8853\uff0c \uf961\u6210\u4efd\u5c0d\u65bc\u8a9e\u97f3\u8fa8\uf9fc\u7684\u91cd\u8981\u6027\uff0c\u56e0\u6b64\u6211\u5011\u5e0c\u671b\u80fd\u628a\u4e00\u7279\u5fb5\u6642\u9593\u5e8f\uf99c\u4e2d\u7684\uf967\u540c\u983b\uf961\u6210\u4efd\u5206 (\u56db) \u807d\u89ba\uf984\u6ce2\u5668\u8a2d\u8a08(auditory filter design) \uf9ea\u51fa\uf92d\uff0c\u9032\u800c\u500b\u5225\u8655\uf9e4\uff0c\u521d\u6b65\u7684\u69cb\u60f3\u662f\u80fd\u5c0d\u65bc\u8abf\u8b8a\u983b\uf961\u8f03\u91cd\u8981\u4e4b\u4f4e\u983b\u7684\u90e8\u4efd\u8f03\u7cbe\u7d30\u7684\u8655 \u4e00\u822c\u800c\u8a00\uff0c\u8a9e\u97f3\u8fa8\uf9fc\u4e2d\u7279\u5fb5\uf96b\uf969\u6c42\u53d6\u7a0b\u5e8f\uf9e8\u6240\u61c9\u7528\u7684\u8a9e\u97f3\u807d\u89ba\uf984\u6ce2\u5668\u7d44\u70ba\u6885\u723e\u5c3a \uf9e4\uff0c\u76f8\u5c0d\u6bd4\u8f03\uf967\u91cd\u8981\u4e4b\u9ad8\u983b\u7684\u90e8\u4efd\u5247\u4f7f\u7528\u8f03\u7c97\uf976\u7684\u65b9\u5f0f\u8655\uf9e4\u3002\u57fa\u65bc\u6b64\u76ee\u7684\uff0c\u6211\u5011\u767c\u73fe\u5c0f \ufa01(mel-scaled)\u7684\uf984\u6ce2\u5668\u7d44\uff0c\u9019\u4e9b\uf984\u6ce2\u5668\u5176\u5206\u4f48\u7279\u6027\u70ba\uff1a1 kHz \u983b\uf961\u4ee5\u4e0b\u70ba\u7dda\u6027\u5206\u4f48\uff0c1 kHz \u6ce2\u8f49\u63db\u662f\u500b\u5341\u5206\u6709\u7528\u7684\u5de5\u5177\uff0c\u512a\u9ede\u70ba\u5176\u80fd\u5c0d\u4e00\u983b\uf961\u5340\u57df\u4f5c\uf967\u7b49\u5206\u7684\ufa00\u5272\uff0c\u5373\u5c07\u8a0a\u865f\u5176\u8f03 \u4ee5\u4e0a\u983b\uf961\u70ba\u975e\u7dda\u6027\u5206\u4f48\uff0c\u5f7c\u6b64\u76f8\u4e92\u90e8\u5206\u91cd\u758a\uff0c\u5176\u53ef\u8fd1\u4f3c\u6a21\u64ec\u4eba\u8033\u807d\u89ba\u6548\u61c9\u3002\u76f8\u5c0d\u800c\u8a00\uff0c \u4f4e\u983b\uf961\u90e8\u5206\u4f7f\u7528\u8f03\u7a84\u7684\uf984\u6ce2\u5668\u904e\uf984\u51fa\uf92d\uff0c\u800c\u9ad8\u983b\u90e8\u5206\u5247\u7528\u8f03\u5bec\u7684\uf984\u6ce2\u5668\u5f97\u4e4b\uff0c\u4e4b\u5f8c\u5c0d\u65bc \u5c0f\u6ce2\u8655\uf9e4\u4e4b\u7814\u7a76\u5b78\u8005[7]\u4e5f\u63d0\u51fa\uf9ba\uf9dd\u7528\u5c0f\u6ce2\u5305(wavelet packet) \u7684\u7279\u6027\uf92d\u4eff\u6548\u4eba\u8033\u807d\u89ba\u6548 \u6bcf\u500b\u5b50\u983b\u5e36\u7684\u7279\u5fb5\u5e8f\uf99c\u4f5c\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u3002\u9019\u6a23\u7684\u7a0b\u5e8f\uff0c\u76f8\u8f03\u65bc\u50b3\u7d71\u7684\u5168\u983b\u5e36\u5f0f\u7684\u7279\u5fb5\u7d71 \u61c9\uff0c\u5176\u9069\u7576\u900f\u904e\u4e00\uf99a\uf905\u5c0f\u6ce2\u5305\u8f49\u63db\u6240\ufa00\u5272\u7684\u90e8\u4efd\u983b\u5e36\uff0c\u9078\u64c7\u51fa\u80fd\u8da8\u8fd1\u65bc\u4eba\u8033\u807d\u89ba\u7684\uf984\u6ce2 \u8a08\u6b63\u898f\u5316\u6cd5\uff0c\uf9e4\u61c9\u53ef\u4ee5\u9032\u4e00\u6b65\u63d0\u6607\u8655\uf9e4\u5f8c\u4e4b\u7279\u5fb5\u7684\u5f37\u5065\u6027\u3002\u4e4b\u5f8c\u4e00\u7cfb\uf99c\u7684\u7ae0\u7bc0\uff0c\u6211\u5011\u5c07 \u5668\u7d44\u6548\u61c9\uff0c\u800c\u7531\u65bc\u5c0f\u6ce2\u8655\uf9e4\u6240\u5f97\u4e4b\u5f7c\u6b64\u983b\u5e36\u9593\u90fd\u5047\u8a2d\u70ba\uf967\u76f8\u95dc\uff0c\u5373\u70ba\u4e92\uf967\u5f71\u97ff\uff0c\u56e0\u6b64\u6240 \u9010\u6b65\u4ecb\u7d39\u5c0f\u6ce2\u8f49\u63db\u4e4b\u5206\u983b\uf9e4\uf941\u4ee5\u53ca\u6240\u63d0\u51fa\u7684\u5206\u983b\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\uff0c\u6700\u5f8c\u4ee5\u5be6\u9a57\u7d50\u679c\u8b49 \ufa00\u5272\u51fa\uf92d\u7684\u5404\u983b\uf961\u7bc4\u570d\u7684\u8a9e\u97f3\u4fe1\u865f\u90fd\u6db5\u84cb\uf9ba\u7368\uf9f7\u7684\u8fa8\uf9fc\u8cc7\u8a0a\uff0c\u5176\u4e2d\u7684\u5be6\u9a57\u7d50\u679c\u9a57\u8b49\uf9ba\u4ee5 \u5be6\u6b64\u5206\u983b\u5f0f\u6b63\u898f\u5316\u6cd5\u512a\u65bc\u50b3\u7d71\u4e4b\u5168\u983b\u5f0f\u6b63\u898f\u5316\u65b9\u6cd5\u3002 \u4e0a\u7684\u8655\uf9e4\u53ef\u4ee5\u512a\u65bc\u50b3\u7d71\u7684\u6885\u723e\uf984\u6ce2\u5668\u7d44\u8655\uf9e4\uff0c\u9054\u5230\u5c07\u8a9e\u97f3\u8fa8\uf9fc\u7cbe\u78ba\ufa01\u63d0\u5347\u7684\u76ee\u7684\u3002 \u85c9\u6b64\ufa09\u4f4e\u96dc\u8a0a\u6216\u901a\u9053\u5e72\u64fe\u6216\u51f8\u986f\u8a9e\u97f3\u7684\u7368\u7279\u6210\u4efd\uff0c\u800c\u9054\u5230\u660e\u986f\u7684\u6539\u9032\u6548\u679c\uff0c\u672c\uf941\u6587\u7684\u7814 \u7a76\u65b9\u5411\uff0c\u5373\u70ba\u958b\u767c\u51fa\u65b0\u7684\ufa09\u4f4e\u96dc\u8a0a\u8207\u901a\u9053\u5e72\u64fe\u4e4b\u76f8\u95dc\u7684\u8a9e\u97f3\u5f37\u5065\u6027\u6f14\u7b97\u6cd5\u3002\u7136\u800c\uff0c\u8ddf\u904e \u5728\u672c\uf941\u6587\u4e2d\uff0c\u6240\u767c\u5c55\u51fa\u7684\u65b0\u6280\u8853\uff0c\u4e26\uf967\u540c\u65bc\u4e0a\u8ff0\u6240\u63d0\u7684\u5e7e\u500b\u50b3\u7d71\u5c0f\u6ce2\u8655\uf9e4\u6240\u61c9\u7528 \u4e09\u3001\u5c0f\u6ce2\u8f49\u63db\u4e4b\u5206\u983b\u6280\u8853\uf9e4\uf941\u7684\u6982\u8ff0 \u7684\u65b9\u5411\uff0c\u800c\u662f\u8457\u91cd\u65bc\u5c07\u5c0f\u6ce2\u8655\uf9e4\u5176\u7279\u6b8a\u7684\u5206\u983b\u6280\u8853\u9069\u7576\u5730\u904b\u7528\u65bc\u8a9e\u97f3\u7279\u5fb5\u6642\u9593\u5e8f\uf99c \u53bb\u76f8\u95dc\u4e4b\u5f37\u5065\u6027\u6280\u8853\u8f03\u70ba\uf967\u540c\u7684\u662f\uff0c\u6211\u5011\u63a1\u7528\uf9ba\u5c0f\u6ce2\u8f49\u63db(wavelet transform)\uff0c\u5c0d\u65bc\u8a9e\u97f3 \u7279\u5fb5\u4e4b\u6642\u9593\u5e8f\uf99c(temporal trajectory)\u52a0\u4ee5\u8655\uf9e4\uff0c\uf92d\u6539\u5584\u8a9e\u97f3\u7279\u5fb5\u7684\u5f37\u5065\u6027\u3002 \u5728\u9019\u4e00\u7ae0\u4e2d\uff0c\u6211\u5011\u5c07\u5c08\u9580\u8a0e\uf941\u5c0f\u6ce2\u8f49\u63db\u904b\u7528\u65bc\uf9ea\u6563\u6642\u9593\u8a0a\u865f(discrete-time signal)\u7684 (temporal trajectory) \u4e0a\uff0c\u7d50\u5408\u5404\u7a2e\u7d71\u8a08\u6b63\u898f\u5316\u7684\u6280\u8853\uff0c\uf92d\u8655\uf9e4\u5c0f\u6ce2\u8f49\u5f8c\u5404\u5b50\u983b\u5e36\u7684\u7279\u5fb5 \u5206\u983b(frequency division) \u6280\u8853\uff0c\u6b64\u61c9\u7b97\u662f\u5c0f\u6ce2\u8f49\u63db\u6700\u5e38\u88ab\u7528\u4ee5\u8655\uf9e4\u8a0a\u865f\u7684\u65b9\u5411\u3002\u9996\u5148\u6211 \u6642\u9593\u5e8f\uf99c\uff0c\u5728\u4e4b\u5f8c\u7684\u7ae0\u7bc0\u4e2d\u6211\u5011\u5c07\u6703\u9010\u6b65\u4ecb\u7d39\u6b64\u65b0\u6280\u8853\uff0c\u5206\u6790\u5176\u4e3b\u8981\u89c0\uf9a3\u3001\u4f5c\u6cd5\u8207\u53ef\u80fd \u5c0f\u6ce2\u76f8\u95dc\uf9e4\uf941\u5728\u8a0a\u865f\u8655\uf9e4\u7684\u7bc4\u7587\u4e2d\u96d6\u5df2\u767c\u5c55\uf969\u5341\uf98e\uff0c\u7136\u800c\u76f8\u5c0d\u65bc\u5176\u4ed6\u8a31\u591a\uf9e4\uf941\u800c\u8a00\uff0c\u61c9 \u5011\u8003\u616e\u4e00\u7d44\u5178\u578b\u96d9\u901a\u9053\u7684\u6b63\u4ea4\u93e1\u50cf\uf984\u6ce2\u5668(quadrature-mirror filter bank, QMF)[15]\uff0c\u5982\u5716 \u512a\u65bc\u50b3\u7d71\u6280\u8853\u7684\u539f\u56e0\uff0c\u4e26\u4ee5\u4e00\u7cfb\uf99c\u7684\u5be6\u9a57\u8b49\u5be6\u6b64\u65b0\u6280\u8853\u76f8\u5c0d\u65bc\u50b3\u7d71\u76f8\u8fd1\u7684\u6280\u8853\u800c\u8a00\uff0c\uf901 \u7528\u65bc\u5728\u8a9e\u97f3\u5f37\u5065\u6027\u8655\uf9e4\u4e4b\uf9b4\u57df\u4e2d\u4ecd\u504f\u5c11\uf969\uff0c\u800c\u5176\u61c9\u7528\u7684\u65b9\u5411\u5927\u81f4\u4e0a\u4e3b\u8981\u5305\u542b\uf9ba\uff1a\u8a9e\u97f3\u5f37 \u672c\uf941\u6587\u5176\u9918\u7684\u7ae0\u7bc0\u6982\u8981\u5982\u4e0b\uff1a\u5728\u7b2c\u4e8c\u7ae0\uf9e8\uff0c\u4ecb\u7d39\u76ee\u524d\u5e38\u7528\u4e4b\u5f37\u5065\u6027\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u4e26 \u5316(speech enhancement)\u3001\u8a9e\u97f3\u7aef\u9ede\u5075\u6e2c(voice activity detection, VAD)\u3001\u5f37\u5065\u6027\u8a9e\u97f3\u7279\u5fb5 \u80fd\u6709\u6548\u63d0\u6607\u8a9e\u97f3\u8fa8\uf9fc\u5728\u96dc\u8a0a\u5e72\u64fe\u74b0\u5883\u4e0b\u7684\u7cbe\u78ba\u6027\u3002 \u4e09\u4e2d\u6240\u793a\uff1a</td></tr><tr><td>(robust speech feature)\u8207\u807d\u89ba\uf984\u6ce2\u5668\u8a2d\u8a08(auditory filter design)\u7b49\u3002\u6211\u5011\u5c07\u5b83\u5011\u7c21\u8ff0\u5982\u4e0b\uff1a \u63a2\u8a0e\u50b3\u7d71\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u4e4b\u53ef\u80fd\u7f3a\u5931\u3002\u5728\u7b2c\u4e09\u7ae0\uff0c\u6211\u5011\u5c07\u7c21\u8981\u4ecb\u7d39\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u4e4b\u5206\u983b\u6280 (\u4e00) \u8a9e\u97f3\u5f37\u5316(speech enhancement) \u8853\u7684\u5be6\u73fe\uff0c\u7b2c\u56db\u7ae0\u70ba\u672c\uf941\u6587\u7684\u91cd\u9ede\uff0c\u6211\u5011\u5c07\u5728\u6b64\u7ae0\u4e2d\u4ecb\u7d39\u6211\u5011\u6240\u63d0\u51fa\u7684\u65b0\u65b9\u6cd5\uff0c\u5373\uf978\u7a2e</td></tr><tr><td>\u8a9e\u97f3\u5f37\u5316\u4e3b\u8981\u76ee\u7684\uff0c\u901a\u5e38\u662f\u5728\u4e00\u6bb5\u8a0a\u865f\u4e2d\uff0c\u5c07\u96dc\u8a0a\u6291\u5236\uff0c\u4e26\u5c07\u8a9e\u97f3\u8a0a\u865f\u6210\u4efd\u5f37\u8abf\u51fa \u8abf\u8b8a\u983b\u8b5c\u57df\u7684\u5206\u983b\u7d71\u8a08\u7279\u5fb5\u88dc\u511f\u6cd5\uff1a\u5206\u983b\u5e36\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u6b63\u898f\u5316\u6cd5\u8207\u5206\u983b\u5e36\u7d71\u8a08\u5716\u7b49</td></tr><tr><td>\uf92d\uff0c\u5e38\u7528\u7684\u65b9\u5f0f\u662f\u5047\u8a2d\u96dc\u8a0a\u5728\u983b\u8b5c(spectrum)\u4e0a\u5177\u6709\u8f03\u70ba\u7a69\u614b(stationary)\u7684\u7279\u6027\uff0c\u5728\u983b\u57df \u5316\u6cd5\uff0c\u4e26\u5c0d\u5176\u521d\u6b65\u6548\u679c\u52a0\u4ee5\u4ecb\u7d39\u3002\u5728\u7b2c\u4e94\u7ae0\uff0c\u6211\u5011\u5c07\u57f7\ufa08\u4e00\u7cfb\uf99c\u7684\u8a9e\u97f3\u8fa8\uf9fc\u5be6\u9a57\uff0c\uf92d\u9a57</td></tr><tr><td>\u4e0a\u5c07\u96dc\u8a0a\u6210\u4efd\u6e1b\u4f4e\uff0c\uf9b5\u5982\u8a2d\u8a08\u4e00\uf984\u6ce2\u5668\uf92d\u904e\uf984\u96dc\u8a0a\u7b49\u3002\u800c\u4ee5\u76ee\u524d\u57fa\u65bc\u5c0f\u6ce2\u7684\u4fe1\u865f\u5f37\u5316\u65b9 \u7d50\uf941\uff0c\u53ca\u672a\uf92d\u53ef\u9032\u4e00\u6b65\u7814\u7a76\u7684\u65b9\u5411\u3002 \u6cd5\uff0c\u5176\u4e2d\u4e4b\u4e00\u70ba Donoho[1]\u5b78\u8005\u6240\u63d0\u51fa\u4f7f\u7528\u5c0f\u6ce2\u6536\u7e2e(wavelet shrinkage)\u7684\u65b9\u5f0f\uff0c\u5176\u65b9\u6cd5 \u8b49\u6240\u63d0\u4e4b\u65b0\u65b9\u6cd5\u8db3\u4ee5\u6709\u6548\u63d0\u6607\u8a9e\u97f3\u7279\u5fb5\u5728\u96dc\u8a0a\u74b0\u5883\u4e0b\u7684\u5f37\u5065\u6027\uff0c\u6700\u5f8c\uff0c\u7b2c\uf9d1\u7ae0\u5247\u70ba\u7c21\u8981 \u5716\u4e09 \u96d9\u901a\u9053 QMF \uf984\u6ce2\u5668\u7d44</td></tr><tr><td>\u662f\u7531\u5c0f\u6ce2\u8f49\u63db\u6240\u5f97\u4e4b\u4fc2\uf969\uff0c\u7d93\u7531\u9580\u6abb\u503c\u7684\u8a2d\u5b9a\u5c07\u96dc\u8a0a\u9069\ufa01\u5730\u6291\u5236\u3002\u5728\u5176\u76f8\u95dc\uf941\u6587\u4e4b\u5be6\u9a57 \u5176\u4e2d</td></tr><tr><td>\u7d50\u679c\u986f\u793a\uf9ba\uff0c\u900f\u904e\u5c0f\u6ce2\u8f49\u63db\u8655\uf9e4\u7684\u8a9e\u97f3\u5f37\u5316\u6548\u80fd\u6bd4\u8d77\u4e4b\u524d\u6240\u63d0\u51fa\u7684\u50b3\u7d71\u8a9e\u97f3\u5f37\u5316\u65b9\u6cd5 \u4e8c\u3001\u5404\u7a2e\u5f37\u5065\u6027\u6280\u8853\u4ecb\u7d39</td></tr><tr><td>[2]\u8981\uf92d\u7684\u597d\u3002 \u5728\u9019\uf9e8\u6211\u5011\u9996\u5148\u76ee\u524d\u5e38\u7528\u4e4b\u5f37\u5065\u6027\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\uff0c\u4e4b\u5f8c\u63a2\u8a0e\u50b3\u7d71\u7d71\u8a08\u6b63\u898f\u5316\u6cd5 (\u4e8c) \u8a9e\u97f3\u7aef\u9ede\u5075\u6e2c(voice activity detection, VAD) \u4e4b\u53ef\u80fd\u7f3a\u5931\uff0c\u4e26\uf96f\u660e\u70ba\u4f55\u4f7f\u7528\u5c0f\u6ce2\u8f49\u63db(discrete wavelet transform, DWT)\u6539\u5584\u9019\u4e9b\u554f\u984c\u3002</td></tr><tr><td>\u7531\u65bc\u4e00\u6bb5\uf93f\u97f3(recording)\uf9e8\u53ef\u80fd\u5305\u542b\u6709\u975e\u8a9e\u97f3\u7684\u5340\u6bb5\uff0c\u5982\u679c\u4e00\u4f75\u8fa8\uf9fc\u6574\u6bb5\uf93f\u97f3\uff0c\u5c07 \u7531\u65bc\u8a9e\u97f3\u8fa8\uf9fc\u7cfb\u7d71\u5bb9\uf9e0\u53d7\u5230\u96dc\u8a0a\u74b0\u5883\u5f71\u97ff\u4f7f\u5f97\u5176\u8fa8\uf9fc\u6548\u80fd\ufa09\u4f4e\uff0c\u56e0\u6b64\u8a9e\u97f3\u8655\uf9e4\u76f8\u95dc</td></tr><tr><td>\u6703\u5f71\u97ff\u8fa8\uf9fc\u8655\uf9e4\u7684\u901f\ufa01\uff0c\u4e26\u53ef\u80fd\u9020\u6210\u8fa8\uf9fc\u7cbe\u78ba\ufa01\u660e\u986f\u4e0b\ufa09\u3002\u8a9e\u97f3\u7aef\u9ede\u5075\u6e2c(voice activity \u7814\u7a76\u7684\u5b78\u8005\u91dd\u5c0d\u6b64\u96dc\u8a0a\u5e72\u64fe\u7684\u554f\u984c\uff0c\u63d0\u51fa\u8af8\u591a\u7684\u5f37\u5065\u6027\u6280\u8853\uff0c\u9019\u4e9b\u6280\u8853\u4e2d\u6709\u4e00\u5927\uf9d0\u662f\u85c9</td></tr><tr><td>detection, endpoint detection)\u76f8\u95dc\u6280\u8853\u5373\u662f\u65bc\u6c7a\u5b9a\u51fa\u4e00\u6bb5\u8a0a\u865f\u4e2d\u771f\u6b63\u8a9e\u97f3\u5b58\u5728\u7684\u4f4d\u7f6e\u3002\u5728 \u7531\u6b63\u898f\u5316\u8a9e\u97f3\u7279\u5fb5\u7684\u7d71\u8a08\u7279\u6027\uff0c\uf92d\ufa09\u4f4e\u96dc\u8a0a\u5c0d\u8a9e\u97f3\u7279\u5fb5\u9020\u6210\u7684\u5931\u771f\u3002\u4ee5\u4e0b\u5c07\u4ecb\u7d39\u8fd1\uf98e\uf92d</td></tr><tr><td>\u50b3\u7d71\u7684\u4f5c\u6cd5\u4e0a\uff0c\u4ee5\u6642\u57df(time domain)\u800c\u8a00\uff0c\u900f\u904e\u8a08\u7b97\u4e00\u6bb5\u8a9e\u97f3\u4fe1\u865f\u7684\u80fd\uf97e(energy)\u6216\u904e\uf9b2 \u5728\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\uf9fc\u4e2d\u5e38\u7528\u7684\u5e7e\u7a2e\u8a9e\u97f3\u7279\u5fb5\u6b63\u898f\u5316\u6280\u8853\u3002\u5176\u4e2d\u5305\u542b\uf9ba\uff1a\u5012\u983b\u8b5c\u5e73\u5747\u6d88\u53bb\u6cd5</td></tr></table>", | |
| "num": null, | |
| "text": "\u95dc\u9375\u8a5e\uff1a\uf9ea\u6563\u5c0f\u6ce2\u8f49\u63db\u3001\u8a9e\u97f3\u8fa8\uf9fc\u3001\u5f37\u5065\u6027\u8a9e\u97f3\u7279\u5fb5\uf96b\uf969keywords: speech recognition, discrete wavelet transform, robust speech features", | |
| "html": null, | |
| "type_str": "table" | |
| }, | |
| "TABREF1": { | |
| "content": "<table><tr><td>\u7279\u5fb5\uf96b\uf96913\u7dad(c0~c12)\uff0c\u52a0\u4e0a\u4e00\u968e\u8207\u4e8c\u968e\u5dee\uf97e\uff0c\u7e3d\u5171\u70ba39\u7dad\u7279\u5fb5\uf96b\uf969\u3002\u5728\u8868\u4e09\u4e2d\uff0c (a) \uf967\u540c\u5f62\u5f0f\u4e4b\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u6b63\u898f\u5316\u6cd5\u7684\u8fa8\uf9fc\uf961\u6bd4\u8f03</td></tr><tr><td>HEQ\u3002\u76f8\u8f03\u65bc\u50b3\u7d71\u7684\u5168\u983b\u5e36\u7d71\u8a08\u88dc\u511f\u6cd5\uff0c\u6211\u5011\u6240\u63d0\u51fa\u4e4b\u5206\u983b\u5e36\u7d71\u8a08\u88dc\u511f \u6cd5\u6709\u4ee5\u4e0b\u5e7e\u9ede\u76f8\uf962\u4e4b\u8655\uff1a 1. \u50b3\u7d71\u7684\u5168\u983b\u5e36 MVN(FB-MVN)\u6cd5\u4e2d\uff0c\u4efb\u4e00\u7279\u5fb5\u5e8f\uf99c\u7684\u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\u901a\u5e38\u5206\u5225\u88ab\u6b63 \u898f\u5316\u70ba 0 \u8207 1\uff0c\u4f46\u5c0d\u65bc SB-MVN \u800c\u8a00\uff0c\uf967\u540c\u5206\u983b\u5e36\u7684\u7279\u5fb5\u5e8f\uf99c\u4e26\uf967\u64c1\u6709\u76f8\u540c\u7684\u76ee\u6a19 \u5e73\u5747\u503c\u8207\u8b8a\uf962\uf969\uff0c\u56e0\u6b64\uf967\u540c\u5206\u983b\u5e36\u7279\u5fb5\u5e8f\uf99c\u5373\u4f7f\u5728\u6b63\u898f\u5316\u5f8c\uff0c\u4ecd\u4fdd\u6709\u5f7c\u6b64\u7d71\u8a08\u7279\u6027 \u7684\u5dee\uf962\u3002\u76f8\u540c\u5730\uff0cSB-HEQ \u4e5f\u662f\u5177\u6709\u6b64\u7279\u6027\uff0c\uf967\u540c\u7684\u5206\u983b\u5e36\u7279\u5fb5\u5e8f\uf99c\u5c0d\u61c9\u81f3\uf967\u540c\u7684 \u76ee\u6a19\u6a5f\uf961\u5206\u4f48\u51fd\uf969\u3002 2. \u5728 SB-MVN \u8207 SB-HEQ \u4e2d\uff0c\u53ef\u4efb\u610f\u9078\u64c7\u67d0\u4e9b\u5206\u983b\u5e36\u5e8f\uf99c\uf92d\u4f5c\u6b63\u898f\u5316\u3002\u4e00\u822c\u800c\u8a00\uff0c\u5c0d \u65bc\u8a9e\u97f3\u8fa8\uf9fc\uf92d\uf96f\uff0c\u4f4e(\u8abf\u8b8a)\u983b\uf961\u7684\u6210\u5206\uff0c\u5305\u542b\u7684\u8a9e\u97f3\u9451\u5225\u8cc7\u8a0a\u8f03\u591a\uff0c\u56e0\u6b64\u6211\u5011\u901a\u5e38 \u512a \u5148 \u9078 \u64c7 \u4f4e \u983b \uf961 \u7684 \u5206 \u983b \u5e36 \u7279 \u5fb5 \u52a0 \u4ee5 \u6b63 \u898f \u5316 \u3002 \u4f46 \u662f \uff0c \u5982 \u679c \u6709 \u4e9b \u975e \u7a69 \u614b \u96dc \u8a0a (non-stationary noise) \u5b58\u5728\u65bc\u9ad8\u8abf\u8b8a\u983b\uf961\u7684\u5340\u57df\uff0c\u70ba\uf9ba\ufa09\u4f4e\u6b64\uf9d0\u96dc\u8a0a\u5e72\u64fe\uff0c\u5c31\u9808\u5c07\u9ad8 \u983b\u7684\u5206\u983b\u5e36\u8003\u616e\u9032\u53bb\u4e00\u540c\u8655\uf9e4\u3002 3. \u7531\u65bc DWT \u7a0b\u5e8f\u4e2d\u7684\ufa09\u4f4e\u53d6\u6a23(down-sampling)\u6b65\u9a5f\uff0c\u6211\u5011\u6240\u9700\u8655\uf9e4\u4e4b\u6240\u6709\u5206\u983b\u5e36\u5e8f \uf99c\u7684\u7279\u5fb5\u7e3d\uf969\u8fd1\u4f3c\u7b49\u540c\u65bc\u539f\u59cb\u5e8f\uf99c\u7684\u7279\u5fb5\u7e3d\uf969\uff0c\u56e0\u6b64\u8655\uf9e4\u4e0a\u4e26\uf967\u6703\u56e0\u70ba\u589e\u52a0\u5206\u983b\u5e36 \u7684\uf969\u76ee\u800c\u4f7f\u8a08\u7b97\u8907\u96dc\ufa01\u5927\u5e45\u63d0\u5347\u3002\u4f46\uf974\u4ee5\u50b3\u7d71\u7684\u5206\u983b\uf984\u6ce2\u5668\u7d44(filter-bank)\u4e4b\u65b9\u6cd5\uff0c \u6240\u9700\u8655\uf9e4\u7684\u7e3d\u7279\u5fb5\uf969\u6703\u660e\u986f\u96a8\u5206\u983b\u5e36\u7684\u500b\uf969\u800c\u589e\u52a0\uff0c\u76f8\u5c0d\u800c\u8a00\uff0c\u5176\u904b\u7b97\u7684\u8907\u96dc\ufa01\u6703 \u56e0\u6b64\u5927\u5e45\u63d0\u9ad8\u3002 (\u4e8c)\u5206\u983b\u5e36\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u7684\u521d\u6b65\u6548\u80fd\u8a0e\uf941 \u5728\u9019\uf9e8\uff0c\u6211\u5011\u5c07\u6240\u63d0\u51fa\u7684\u5206\u983b\u5e36\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u8ddf\u539f\u59cb\u4e4b\u5168\u983b\u5e36\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u4f5c\u521d\u6b65 \u7684\u6548\u80fd\u6bd4\u8f03\uff0c\u6839\u64da\u9019\u4e9b\u65b9\u6cd5\u5728\u4e00\u8a9e\u97f3\u7279\u5fb5\u5e8f\uf99c\u4e4b\u8abf\u8b8a\u983b\u8b5c\u7684\u5931\u771f\u6539\u5584\u7a0b\ufa01\uff0c\uf92d\u8a55\u4f30\u9019\u4e9b Hz]\u548c[25 Hz, 50 Hz])\u5247\u7dad\u6301\uf967\u52d5\uff0c\u800c SB-MVN (1,2,3,4) \u8207 SB-HEQ (1,2,3,4) \u8868\u793a\uf9ba\u5168\u90e8\u56db\u500b\u5206 \u983b\u5e36\u7686\u500b\u5225\u4ee5 MVN \u6216 HEQ \u8655\uf9e4\u3002 \u9996\u5148\uff0c\u6211\u5011\u5c0d\u65bc\u5168\u983b\u5e36\u8207\u5404\u7a2e\u5206\u983b\u5e36\u4e4b MVN \u6cd5\u7684\u8655\uf9e4\u7d50\u679c\u52a0\u4ee5\u8a0e\uf941\u3002\u5716\u4e03 (a)(b)(c)(d)\u5206\u5225\u8868\u793a\u70ba\u539f\u59cb\u672a\u8655\uf9e4\u4e4b\u7b2c\u4e00\u7dad MFCC( 1 c )\u7279\u5fb5\u5e8f\uf99c\u3001FB-MVN\u3001SB-MVN (1,2) \u8207 SB-MVN (1,2,3,4) \u8655\uf9e4\u5f8c\u4e4b 1 c \u5e8f\uf99c\u4e4b\u529f\uf961\u983b\u8b5c\u5bc6\ufa01(power spectral density, PSD)\u66f2\u7dda\u3002 \u5728\u5716\u4e03(a)\u4e2d\uff0c\u53ef\u770b\u51fa\uf967\u540c SNR \u503c\u4e0b(clean, 10 dB \u8207 0dB)\u4e4b\u672a\u8655\uf9e4\u904e\u7684 1 c \u5e8f\uf99c\uff0c\u5176 PSD \u66f2\u7dda\uff0c\u53d7\u5230\u52a0\u6210\u6027\u96dc\u8a0a(additive noise) \u7684\u5f71\u97ff\uff0c\u5b58\u5728\u56b4\u91cd\u7684\u5931\u771f\u60c5\u5f62\u3002\u800c\u7d93\u7531\u5716\u4e03 (b)\u53ef\u770b\u51fa\uff0cFB-MVN \u8655\uf9e4\u5f8c\u4e4b 1 c \u5e8f\uf99c\uff0c\u5728\u8f03\u4f4e\u7684\u8abf\u8b8a\u983b\uf961[0, 10 Hz]\u4e4b\u9593\uff0c\u5176 PSD \u5931\u771f \u7684\u60c5\u6cc1\u5f88\u660e\u986f\ufa09\u4f4e\uff0c\u4f46\u5728\u9ad8\u8abf\u8b8a\u983b\uf961\u7bc4\u570d[10Hz, 50 Hz]\uff0cPSD \u5931\u771f\u7684\u60c5\u5f62\u4e26\u6c92\u6709\u592a\u5927\u7684 \u6539\u5584\u3002\u5716\u4e03(c)\u70ba SB-MVN (1,2) \u6240\u5f97\u4e4b\u7279\u5fb5\u5e8f\uf99c\u4e4b PSD \u5716\uff0c\u5176\u6240\u8655\uf9e4\u7684\u983b\u5e36\u5206\u5225\u70ba[0, 6.25 Hz]\u548c[6.25 Hz, 12.5 Hz] \uff0c\u5f9e\u6b64\u5716\u53ef\u4ee5\u767c\u73fe\uff0c\u7d04\u5728\u8abf\u8b8a\u983b\uf961 20 Hz \u4ee5\u4e0b\uff0c\u5176 PSD \u5931\u771f\u60c5 \u5f62\u76f8\u5c0d\u6e1b\u4f4e\uff0c\u4f46\u5728\u672a\u8655\uf9e4\u7684\u8abf\u8b8a\u983b\uf961\u7bc4\u570d[12.5 Hz, 50 Hz]\uff0c\u540c\u6a23\u5b58\u6709\u660e\u986f\u7684\u5931\u771f\u60c5\u6cc1\u3002 \u5716\u4e03(d)\u70ba SB-MVN (1,2,3,4) \u6240\u5f97\u4e4b\u7279\u5fb5\u5e8f\uf99c\u4e4b PSD \u5716\uff0c\u5176\u6240\u8655\uf9e4\u7684\u983b\u5e36\u5206\u5225\u70ba[0, 6.25 Hz]\u3001[6.25 Hz, 12.5 Hz]\u3001[12.5 Hz, 25 Hz]\u8207[25 Hz, 50 Hz]\uff0c\u5f88\u660e\u986f\u53ef\u770b\u51fa\u5728\u5168\u90e8\u7684\u8abf\u8b8a \u983b\uf961\u7bc4\u570d\uff0c\u5176 PSD \u5931\u771f\u7684\u60c5\u6cc1\u7686\u6709\u6548\ufa09\u4f4e\u3002 \u63a5 \u4e0b \uf92d \uff0c \u5716 \u516b (a)(b)(c)(d) \u5206 \u5225 \u8868 \u793a \u70ba \u539f \u59cb \u672a \u8655 \uf9e4 \u4e4b \u7b2c \u4e00 \u7dad MFCC( 1 c ) \u7279 \u5fb5 \u5e8f \uf99c \u3001 FB-HEQ\u3001SB-HEQ (1,2) \u8207 SB-HEQ (1,2,3,4) \u8655\uf9e4\u5f8c\u4e4b 1 c \u5e8f\uf99c\u4e4b PSD \u66f2\u7dda\uff0c\u5176\u4e2d\u62ec\u5f27\u4e2d\u7684\uf969\u5b57 \u8868\u793a\u6240\u8655\uf9e4\u7684\u983b\u5e36\u3002\u6bd4\u8f03\u5716\u516b(a)\u8207\u5716\u516b(b)\u53ef\u77e5\uff0c\u5c0d\u65bc\u8f03\u4f4e\u7684\u8abf\u8b8a\u983b\uf961\u7bc4\u570d[0, 10 Hz] \uff0c \u7686\u5df2\u6709\u6548\ufa09\u4f4e\u3002\uf9d0\u4f3c\u4e4b\u524d\u7684\uf9fa\u6cc1\uff0c\u7576\u6bd4\u8f03\u5716\u516b(d)\u8207\u5716\u4e03(d)\u6642\uff0c\u53ef\u770b\u51fa SB-HEQ (1,2,3,4) \u5728 \ufa09\u4f4e PSD \u5931\u771f\u7684\u6027\u80fd\u4e0a\u512a\u65bc SB-MVN (1,2,3,4) \u3002 \u5716\u516b (a) \u539f\u59cb 1 c \u7279\u5fb5\u5e8f\uf99c\u3001(b)FB-HEQ\u3001(c)SB-HEQ (1,2) \u8207(d)SB-HEQ (1,2,3,4) \u4f5c\u7528\u5728\uf967\u540c\u8a0a \u96dc\u6bd4\u4e0b\u4e4b 1 c \u7279\u5fb5\u5e8f\uf99c\u4e4b\u529f\uf961\u983b\u8b5c\u5bc6\ufa01\u66f2\u7dda\u5716 \u4e94\u3001\u8abf\u8b8a\u983b\u8b5c\u5206\u983b\u5e36\u6b63\u898f\u5316\u6cd5\u7684\u8fa8\uf9fc\u5be6\u9a57\u7d50\u679c\u8207\u8a0e\uf941 \u672c\u7ae0\u4e3b\u8981\u5167\u5bb9\u70ba\u5448\u73fe\u4e26\u5206\u6790\u4e00\u7cfb\uf99c\u7684\u5f37\u5065\u6027\u7279\u5fb5\u6280\u8853\u6240\u5f97\u4e4b\u8a9e\u97f3\u8fa8\uf9fc\u7684\u6548\u679c\uff0c\u9019 \u4e9b\u6280\u8853\u5305\u62ec\uf9ba\u50b3\u7d71\u7684\u5168\u983b\u5f0f\u7279\u5fb5\u7d71\u8a08\u6b63\u898f\u5316\u6cd5\u3001\u6211\u5011\u6240\u65b0\u63d0\u51fa\u7684\u5206\u983b\u5f0f MVN(SB-MVN) \u6cd5\u8207\u5206\u983b\u5f0f HEQ(SB-HEQ)\u6cd5\u3002 (\u4e00)\u5be6\u9a57\u74b0\u5883\u8207\u67b6\u69cb\u8a2d\u5b9a \u672c \u8fa8 \uf9fc \u5be6 \u9a57 \u6240 \u63a1 \u7528 \u7684 \u8a9e \u97f3 \u8cc7 \uf9be \u5eab \u70ba \u6b50 \u6d32 \u96fb \u4fe1 \u6a19 \u6e96 \u5354 \u6703 (European \u8fa8\uf9fc\u7684\u5f71\u97ff\u3002\u7531\u65bc\u96dc\u8a0a\u7684\uf967\u540c\uff0c\u6e2c\u8a66\u74b0\u5883\u53ef\u5206\u70ba Set A\u3001Set B \u8207 Set C \u4e09\u7d44\u3002 \u5728\u8fa8\uf9fc\u4e2d\u6240\u4f7f\u7528\u7684\u8072\u5b78\u6a21\u578b\u662f\u7531\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b\u5de5\u5177(Hidden Markov Model Tool Kit, HTK)[17] (\u4e8c)\u5168\u983b\u5e36\u88dc\u511f\u6cd5\u8207\u5404\u7a2e\u5206\u983b\u5e36\u6b63\u898f\u5316\u6cd5\u4e4b\u5be6\u9a57\u7d50\u679c SB-MVN(1,2,3) SB-MVN(1,2,3,4) \u6211\u5011\u5448\u73fe\uf9ba\u57fa\u790e\u5be6\u9a57(baseline)\u8868\u4e09\u3001\u5404\u5206\u983b\u5e36\u65b9\u6cd5\u8207\u5168\u983b\u5e36\u65b9\u6cd5\u7684\u5e73\u5747\u8fa8\uf9fc\uf961(%)\u8207\u76f8\u5c0d\u932f\u8aa4\ufa09\u4f4e\uf961(%) Method Set A Set B Set C Avg. RR1 RR2 Baseline 71.92 68.22 77.61 71.58 --FB-MVN 85.03 85.56 85.60 85.36 48.49 -SB-MVN (1) 86.87 87.90 87.37 87.38 55.59 13.80 SB-MVN (1,2) 87.28 90.23 89.44 88.89 60.91 24.11 SB-MVN (1,2,3) 89.44 90.31 89.61 89.82 64.18 30.46 SB-MVN (1,2,3,4) 89.47 90.31 89.62 89.84 64.25 30.60 FB-HEQ 87.59 88.84 87.64 88.10 58.13 -SB-HEQ (1) 87.70 89.31 87.81 88.37 59.08 2.27 SB-HEQ (1,2) 89.22 90.55 90.23 89.95 64.64 15.55 SB-HEQ (1,2,3) 89.51 90.75 89.54 90.01 64.85 16.05 SB-HEQ (1,2,3,4) 89.51 90.83 89.57 90.05 64.99 16.39 \u8868\u56db\u3001\u6240\u6709\uf967\u540c SNR \u503c\u96dc\u8a0a\u74b0\u5883\u4e0b\u7684\u5e73\u5747\u8fa8\uf9fc\uf961(%) Method clean 20dB 15dB 10dB 5dB 0dB -5dB Baseline 99.79 95.80 88.15 73.81 56.32 43.82 40.13 FB-MVN 99.82 98.73 96.83 91.88 79.52 59.80 46.70 SB-MVN (1) 99.79 98.67 96.98 92.24 82.42 66.60 51.95 SB-MVN (1,2) 99.80 98.97 97.76 94.33 86.40 70.99 53.80 SB-HEQ (1,2,3,4) 99.64 98.85 97.69 94.74 87.15 71.83 54.01 84 85 86 87 88 89 90 91 Reccognition Accuracy(%) FB-MVN SB-MVN(1) SB-MVN(1,2) (b) \uf967\u540c\u5f62\u5f0f\u4e4b\u7d71\u8a08\u5716\u7b49\u5316\u6cd5\u7684\u8fa8\uf9fc\uf961\u6bd4\u8f03 84 85 86 87 88 89 90 91 Recognition Accuracy(%) FB-HEQ SB-HEQ (1) SB-HEQ (1,2) SB-HEQ(1,2,3) SB-HEQ (1,2,3,4) \u5716\u4e5d \u5404\u5206\u983b\u5e36\u65b9\u6cd5\u8207\u5168\u983b\u5e36\u65b9\u6cd5\u7684\u5e73\u5747\u8fa8\uf9fc\uf961(%)\u4e4b\u7d9c\u5408\u6bd4\u8f03\u5716 \u65b9\u6cd5\u7684\u6548\u80fd\u3002\u6211\u5011\u4f7f\u7528 AURORA-2 Hz, 25 \u5716\u4e03 (a) \u539f\u59cb 1 c \u7279\u5fb5\u5e8f\uf99c\u53ca(b)FB-MVN\u3001(c)SB-MVN (1,2) \u8207(d)SB-MVN (1,2,3,4) \u4f5c\u7528\u5728\uf967\u540c \u8a0a\u96dc\u6bd4\u4e0b\u4e4b 1 15 dB\u300110 dB\u30015 dB\u30010 dB \u8207-5 dB\uff0c\u56e0\u6b64\u6211\u5011\u53ef\u4ee5\u89c0\u5bdf\u5206\u6790\uf967\u540c\u96dc\u8a0a\u74b0\u5883\u4e0b\u5c0d\u65bc\u8a9e\u97f3 SB-HEQ (1,2,3) 99.66 98.84 97.70 94.68 87.09 71.74 53.78 c \u7279\u5fb5\u5e8f\uf99c\u4e4b\u529f\uf961\u983b\u8b5c\u5bc6\ufa01\u66f2\u7dda\u5716 Telecommunication S tandard Institute, ETSI) \u6240\u767c\ufa08\u7684\u8a9e\uf9be\u5eab AURORA-2[16]\uff0c\u5167\u5bb9\u662f\u4ee5 \u7f8e\u570b\u6210\uf98e\u7537\uf981\u6240\uf93f\u88fd\u7684\u4e00\u7cfb\uf99c\uf99a\u7e8c\u7684\u82f1\u6587\uf969\u5b57\u5b57\uf905\uff0c\u6e2c\u8a66\u8a9e\u97f3\u672c\u8eab\u52a0\u4e0a\u5404\u7a2e\u52a0\u6210\u6027\u96dc\u8a0a SB-MVN (1,2,3) 99.78 98.99 97.75 94.57 86.59 71.19 53.69 SB-MVN (1,2,3,4) 99.81 98.97 97.75 94.51 \u5f9e\u8868\u4e09\u3001\u8868\u56db\u548c\u5716\u4e5d\u53ef\u767c\u73fe\uff0c\u6211\u5011\u6240\u65b0\u63d0\u51fa\u7684\u5206\u983b\u5e36\u6b63\u898f\u5316\u6cd5\uff0c\u78ba\u5be6\u80fd\u6709\u6548\u63d0\u6607\u5176 86.60 71.34 53.72 \u96dc\u8a0a\u74b0\u5883\u4e0b\u7684\u5f37\u5065\u6027\uff0c\u5176\u8a73\u7d30\u73fe\u8c61\u5982\u4ee5\u4e0b\u5e7e\u9ede\uff1a \u6216\u901a\u9053\u6548\u61c9\u7684\u5e72\u64fe\u3002\u52a0\u6210\u6027\u96dc\u8a0a\u5171\u6709\u516b\u7a2e\uff0c\u5206\u5225\u662f\u5730\u4e0b\u9435(subway)\u3001\u4eba\u8072(babble)\u3001\u6c7d\uf902 \u6de8\u7121\u96dc\u8a0a\u7684\uf9fa\u614b(clean)\uff0c\u4ee5\u53ca\uf9d1\u7a2e\uf967\u540c\u96dc\u8a0a\u6bd4(signal to noise ratio, SNR)\uff0c\u5206\u5225\u662f 20 dB\u3001 SB-HEQ (1,2) 99.64 98.84 97.64 94.50 87.52 71.28 53.65 station)\u96dc\u8a0a\u7b49\uff1b\u800c\u901a\u9053\u6548\u61c9\u6709\uf978\u7a2e\uff0c\u5206\u5225\u70ba G712 \u548c MIRS\u3002\u96dc\u8a0a\u6bd4\uf9b5\u7684\u5927\u5c0f\u5305\u542b\uf9ba\u4e7e SB-HEQ (1) 99.72 98.72 97.31 93.34 83.98 68.49 52.70 (car)\u3001\u5c55\u89bd\u6703\u9928(exhibition)\u3001\u9910\u5ef3(restaurant)\u3001\u8857\u9053(street)\u3001\u98db\u6a5f\u5834(airport)\u548c\u706b\uf902\u7ad9(train FB-HEQ 99.77 99.01 97.76 94.22 84.30 65.21 48.96 1.</td></tr><tr><td>\u672c\u7ae0\u7bc0\u5be6\u9a57\u63a1\u7528\u6885\u723e\u5012\u983b\u8b5c\u4fc2\uf969(mel-frequency cepstral coefficients, MFCC)</td></tr></table>", | |
| "num": null, | |
| "text": "\u8cc7\uf9be\u5eab[20]\uf9e8\u7684 MAH_27O6571A \u8a9e\u97f3\u6a94\uff0c\u7136\u5f8c\u52a0\u5165 \uf967\u540c\u8a0a\u96dc\u6bd4(SNR)\u7684\u5730\u4e0b\u9435(subway)\u96dc\u8a0a\uff0c\u7e7c\u800c\u52a0\u4ee5\u8655\uf9e4\u3002 \u5728\u6211\u5011\u6240\u63d0\u51fa\u4e4b\u65b9\u6cd5\u4e2d\uff0c\u521d\u6b65\u4f7f\u7528\uf9ba\u4e09\u968e\u7684 DWT \u8f49\u63db\uff0c\u5c07\u6574\u500b\u8abf\u8b8a\u983b\u5e36[0, 50 Hz] \ufa00\u5272\u51fa\u56db\u7a2e\u5206\u983b\u5e36\u7bc4\u570d\uff0c\u5206\u5225\u662f[0, 6.25 Hz] \u3001[6.25 Hz, 12.5 Hz] \u3001[12.5 Hz, 25 Hz]\u548c[25 Hz, 50 Hz]\uff0c (\u7531\u65bc\u7279\u5fb5\u97f3\u6846\u53d6\u6a23\uf961\u70ba 100 Hz\uff0c\u56e0\u6b64\u7279\u5fb5\u5e8f\uf99c\u6db5\u84cb\u4e4b\u983b\uf961\u7bc4\u570d\u70ba[0, 50 Hz]) \u3002 \u5728\u4e4b\u5f8c\u8a0e\uf941\u7684\u6bcf\u500b\u983b\u5e36\u4e4b\u6b63\u898f\u6cd5\u4e2d\uff0c\u6211\u5011\u5728\u65b9\u6cd5\u540d\u7a31\u53f3\u4e0b\u65b9\u4f7f\u7528\u4e0b\u6a19\uf969\u5b57\uf92d\u8868\u793a\u88ab\u6b63\u898f \u5316\u7684\u983b\u5e36\uff0c\uf9b5\u5982 SB-MVN (1,2) \u8207 SB-HEQ (1,2) \u8868\u793a\uf9ba\u7b2c\u4e00\u500b\u5206\u983b\u5e36([0, 6.25 Hz] )\u8207\u7b2c\u4e8c \u500b\u5206\u983b\u5e36([6.25 Hz, 12.5 Hz])\u4f7f\u7528\uf9ba MVN \u6216 HEQ \u8655\uf9e4\uff0c\u5269\u9918\u7684\uf978\u500b\u9ad8\u983b\u5e36([12.5 FB-HEQ \u53ef\u6709\u6548\ufa09\u4f4e PSD \u4e4b\u5931\u771f\uff0c\u4f46\u5c0d\u65bc\u5176\u4ed6\u8abf\u8b8a\u983b\uf961\u7bc4\u570d[10 Hz, 50 Hz] \uff0cPSD \u5931\u771f \u7684\u60c5\u5f62\u4e26\u6c92\u6709\u7372\u5f97\u592a\u5927\u7684\u6539\u5584\u3002\u5716\u516b(c)\u70ba SB-HEQ (1,2) \u6240\u5f97\u4e4b\u7279\u5fb5\u5e8f\uf99c\u4e4b PSD \u5716\uff0c\u5176\u6240 \u8655\uf9e4\u7684\u983b\u5e36\u5206\u5225\u70ba[0, 6.25 Hz] \u8207[6.25 Hz, 12.5 Hz] \uff0c\u5728\u6b64\u5716\u4e2d\uff0c\u53ef\u4ee5\u767c\u73fe\u7d04\u5728\u8abf\u8b8a\u983b\uf961 20 Hz \u4ee5\u4e0b\u4e4b PSD \u5931\u771f\u73fe\u8c61\u76f8\u5c0d\u88ab\u6e1b\u4f4e\uff0c\u4f46\u5728\u5176\u4ed6\u8abf\u8b8a\u983b\uf961\u7bc4\u570d\uff0c\u4ecd\u6709\u660e\u986f\u7684\u5931\u771f\u60c5 \u6cc1\u3002\u8ddf\u4e4b\u524d\u5716\u4e03(c)SB-MVN (1,2) \u7684\u6548\u679c\u6bd4\u8f03\uff0c\u53ef\u770b\u51fa SB-HEQ (1,2) \u512a\u65bc SB-MVN (1,2) \uff0c\uf901 \u6709\u6548\ufa09\u4f4e\u7d04\u5728\u983b\uf961 20 Hz \u4ee5\u4e0b\u7684 PSD \u5931\u771f\ufa01\u3002\u5716\u516b(d)\u70ba SB-HEQ (1,2,3,4) \u6240\u5f97\u4e4b\u7279\u5fb5\u5e8f\uf99c \u4e4b PSD \u5716\uff0c\u5176\u6240\u8655\uf9e4\u7684\u983b\u5e36\u500b\u5225\u70ba[0, 6.25 Hz]\u3001[6.25 Hz, 12.5 Hz]\u3001[12.5 Hz, 25 Hz]\u8207 [25 Hz, 50 Hz] \uff0c\u5f9e\u6b64\u5716\u5f88\u660e\u986f\u53ef\u770b\u51fa\u5168\u90e8\u7684\u8abf\u8b8a\u983b\uf961\u7bc4\u570d\u4e4b PSD \u66f2\u7dda\uff0c\u5176\u5931\u771f\u7684\u60c5\u6cc1 \u8a13\uf996\u800c\u5f97\uff0c\u5305\u62ec\uf9ba 11 \u500b\uf969\u5b57\u6a21\u578b(zero, one, two,\u2026, nine \u53ca oh)\u4ee5\u53ca\u975c\u97f3 (silence)\u6a21\u578b\uff0c\u6bcf\u500b\uf969\u5b57\u6a21\u578b\u5247\u6709 16 \u500b\uf9fa\u614b\uff0c\u5404\uf9fa\u614b\u5305\u542b 20 \u500b\u9ad8\u65af\u5bc6\ufa01\u6df7\u5408\u3002 \u3001\u5404\u7a2e\u5206\u983b\u5f0f SB-MVN \u8207 SB-HEQ\u3001\u5168\u983b\u5f0f FB-MVN \u548c FB-HEQ \u4f5c\u7528\u5728\u539f\u59cb MFCC \u7279\u5fb5\u4e0a\u6240\u5f97\u7684\u5e73\u5747\u8fa8\uf9fc\u7d50\u679c(\uf967\u540c\u7a2e\u8fa8\uf9fc\u74b0\u5883\u7684\u5e73\u5747\u8fa8\uf9fc \uf961\u53ca\u76f8\u5c0d\u6539\u5584\uf961) \uff0c\u5176\u4e2d RR1\u548c RR2\u5206\u5225\u70ba\u76f8\u8f03\u65bc\u57fa\u790e\u5be6\u9a57\u548c\u5168\u983b\u5e36\u6cd5\u4e4b\u76f8\u5c0d\u932f\u8aa4\ufa09\u4f4e \uf961(relative error rate reductions)\u3002\u8868\u56db\uf99c\u51fa\u5728\u5404\u7a2e\uf967\u540c\u7684 SNR \u503c\u4e0b\u7684\u5404\u7a2e\u65b9\u6cd5\u7684\u5e73\u5747\u8fa8 \uf9fc\uf961\uff0c\u800c\u5716\u4e5d\u7c21\u8981\u756b\u51fa\u5404\u65b9\u6cd5\u5e73\u5747\u8fa8\uf9fc\uf961\u7684\u6bd4\u8f03\u5716\u3002 \u7121\uf941\u5168\u983b\u5e36\u8207\u5206\u983b\u5e36\u6b63\u898f\u5316\u65b9\u6cd5\uff0c\u76f8\u8f03\u65bc\u57fa\u672c\u5be6\u9a57\u800c\u8a00\uff0c\u90fd\u6709\uf97c\u597d\u7684\u6539\u5584\u6548\u80fd\uff0c\u76f8 \u5c0d\u932f\u8aa4\ufa09\u4f4e\uf961\u90fd\u5728 48%\u4ee5\u4e0a\uff0c\u9664\u6b64\u4e4b\u5916\uff0c\u6bcf\u4e00\u7a2e HEQ \u7684\u6548\u679c\u90fd\u6bd4\u5176\u76f8\u540c\u5f62\u5f0f\u7684 MVN \uf92d\u7684\u597d\u3002\u76f8\u8f03\u65bc MVN\uff0cHEQ \u984d\u5916\u5c0d\u65bc\u7279\u5fb5\u9ad8\u968e\u52d5\u5dee\u505a\u88dc\u511f\u8655\uf9e4\uff0c\u6240\u4ee5\u6574\u9ad4 \uf92d\uf96f\uff0cHEQ \uf901\u6709\u52a9\u65bc\u6539\u5584\u96dc\u8a0a\u74b0\u5883\u6240\u9020\u6210\u7684\u7279\u5fb5\u5931\u771f\u3002 2. SB-MVN \u7684\u56db\u7a2e\u5206\u983b\u6a21\u5f0f\u6548\u80fd\u90fd\u512a\u65bc\u539f\u59cb\u5168\u983b\u5f0f\u7684 FB-MVN\uff0c\u6b64\u60c5\u6cc1\u5728 SB-HEQ \u8207 FB-HEQ \u4e4b\u9593\u7684\u6bd4\u8f03\u4e5f\u662f\u5982\u6b64\u3002\u800c SB-MVN \u548c SB-HEQ \u76f8\u8f03\u65bc\u539f\u59cb FB-MVN \u548c FB-HEQ \u7684\u76f8\u5c0d\u932f\u8aa4\ufa09\u4f4e\uf961\u5206\u5225\u9ad8\u9054 30.60%\u8207 16.39%\uff0c\u6b64\u7d50\u679c\u986f\u793a\u6240\u63d0\u51fa\u7684\u65b0\u5206 \u983b\u8655\uf9e4\u6280\u8853\u512a\u65bc\u50b3\u7d71\u5168\u983b\u5e36\u7684\u8655\uf9e4\uff0c\u56e0\u6b64\u6211\u5011\u6210\u529f\u7684\u9a57\u8b49\uf9ba\u4e4b\u524d\u7ae0\u7bc0\u7684\u63a8\uf941\uff0c\u5373\uf967 \u540c\u7684\u8abf\u8b8a\u983b\u8b5c\u6210\u4efd\u5c0d\u65bc\u8a9e\u97f3\u8fa8\uf9fc\u6709\uf967\u540c\u7684\u91cd\u8981\u6027\uff0c\u5c0d\uf967\u540c\u983b\u5e36\u5206\u5225\u4f5c\u88dc\u511f\u53ef\u5e36\uf92d\uf901 \u597d\u7684\u6548\u80fd\u3002 3. \u5f9e\u8868\u56db\u4e2d\u89c0\u5bdf\u5728\uf967\u540c SNR \u503c\u60c5\u6cc1\u4e0b\u7684\u5e73\u5747\u8fa8\uf9fc\uf961\uff0c\u6211\u5011\u53ef\u77e5\u5728\uf967\u53d7\u4efb\u4f55\u96dc\u8a0a\u5e72\u64fe", | |
| "html": null, | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |