20 6 JO URAL O F CH IESE IFO RM AT IO PROCESS IG Vol120 o16 : 1003-0077 (2006) 06-0089 - 08,,, (, 230027) : 100,,, 500,, (2144)(2130) :; ;; ; ; : TP391: A The Electron ic PSC Testing System W E I Si, L IU Q ing2sheng, HU Yu, WAG Ren2hua ( Man Machine Voice Communication Laboratory, University of Science&Technology of China, Hefei, Anhui 230027, China) Abstract: This paper develop s an automatic PSC testing system aim ing at efficiently evaluating the spoken Chinese. On the basis of 100 hoursstandard Chinese database, this paper uses the characteristic of Chinese and linguist s expert knowledge to op tim ize the traditional speech evaluation algorithm. A t the same time, a corpus2adap tive method is p ro2 pose to enhance the robustness and performance of the algorithm. Experiments on 500 personspsc testing database p rove that the new algorithm is much better than the original algorithm. After linear mapp ing, the error between the machine score and the human score is almost equal to the error between humans, that is 2144. The result indicates that the automatic PSC testing system can rep lace the human to evaluating spoken Chinese under text2dependent con2 dition. Key word: computer app lication; Chinese information p rocessing; Putonghua shuip ing ceshi; p ronunciation evalua2 tion; PSC testing database, automatic testing system. 1,,,,,, 100,,,, : 2005211202 : 2006206219 : ( ZD I105 - B02) : (1982),,,. 89
( SR I) V ILT [ 1, 2 ], SR I SC ILL [ 3, 4 ], V ICK [ 5, 6 ],,, [ 9, 10 ],,,,,,,,,,,,(2144) (2130),,, 2,, 1 1 1,,,, 90
3 311,,,,,, 16K, 16bit 1 1 30 15 15 203025 305 3000/ 4500/ 60/( 400) 3 /, 100,,,, 312,,,,, 16K, 16bit 2 2 500 236 290 259 251 120 84 3 223 277 6% 71% 23%,,, 3, 313 ( ),,264,236 3 A, B { S i, i = 1, 2,, n},(1) : [ (S A i - S A ) (S B i - S B ) ] (S A i - S A ) 2 [ (S B i - S B ) 2 (1) 91
, S A i A i, S B i B i, S A A, S B B 4 ( )/ 1 2 3 1 2 3 (110, 010) / (110, 010) (0191, 1188) / ( 0190, 1197) (0188, 2154) / ( 0189, 2147) (0191, 1188) / ( 0190, 1197) (110, 010) / (110, 010) (0191, 2119) / ( 0189, 2147) (0190, 2120) / (0189, 2130) (0188, 2154) / ( 0189, 2147) (0191, 2119) / ( 0189, 2147) (110, 010) / (110, 010) 4,018,3 4,, 411 HMM, 25m s, 10m s MFCC,39 HMM, TO P O T, P O T O HMM T,HMM [ 13, 5 ] O TP T O,, P P ( T O ) T O (2) [ 3 ] = ( log ( P ( T i O ( T i ) ) ) / F ( T i ) ) / = ( log ( P (O ( T i ) T i ) P ( T i ) qq P (O ( T i ) q) p ( q) ) / F ( T i ) ) / (2) P (O ( T i ) T i ) ( log ( max qq P (O ( T i ) q) ) / F ( T i ) ) /, Q, qt i, F ( T i )T i,, P (O ( T i ) T i ) T i O ( T i ), : 0158,0188 412 41211 92 (2),,
,,,,,, (3) : P ( T O ) = ( log ( P ( T i O ( T i ) ) ) / F ( T i ) ) / = ( log ( P (O ( T i ) T i ) P ( T i ) T qq i error P (O ( T i ) q) p ( q) ) / F ( T i ) ) / (3) P (O ( T i ) T i ) ( log ( max T qq i P (O ( T i ) q) ) / F ( T i ) ) / error (3) (2), ( 2), (3) [ 7 ], 41212, [ 8 ],(4) G sen t = G i / G i = G i in itia l + G i fina l G sent, G i i G i in itia l i G i fina l i,,, (5) G sen t = i / G G i = G i in itia l (1 + D u ri f ina l D u r i in itia l CO EF) + G i fina l G sen t, G i i, D ur i fina l i, D ur i in itia l i CO EF,, CO EF,, 41213,,,, MLLR (Maximum L ikelihood L ine2 ar Regression) [ 11 ], MLLR,,,,,,, : (4) (5) 93
,, T i HTKHMM,,, (6), T i THR ESH i T i < THR ESH i THR ESH,,, MLLR, 5, 511 (3),,,, HMM,, 5 5 / 0165 /0161 0177 /0173 512 (6) 5,,,,,, [ 8 ],, 6, HMM, /, 6 0177 /0173 0181 /0177 6,,[ 8 ] 513 41213,7 7, / HMM, 0177 /0173 0178 /0173 0182 /0179 94
7,, 514,,, 8: HMM,,, 8 8 (/) + + VS 0165 /0161 0183 /0181 VS 0190 /0189 8,, 6,,,,,,,, :,, (7) S core m ach ine S core m ach ine S core m ach ine = 3 = 3 = 3 1i P ( o i ) + S core 4 C = 2i P ( o i ) + S core 4 C = 3i P ( o i ) + S core 4 C, P ( o i )i,, 1 i, Score 4, C, Score m ach ine 9 9 ( )/ + + VS (0183, - ) / (0181, - ) (0195, 1128) / (0184, 2144) VS (0190, 2120) / (0189, 2130) 9,,,, (7) 95
,, (2144)(2130) 7,,,,,,,, 0165 /0161 (/, )0183 /0181,,, 0195 /0184,1128 /2144, 0190 /01892120 /2130,,,,,,, : [ 1 ] H. L. Franco, L. eumeyer, Y. Kim, O. Ronen. Automatic p ronunciation scoring for language instruction[a ]. ICASSP[ C ], 1997, 1465-1468. [ 2 ] L. eumeyer, H. Franco, V. D igalakis, M. W eintraub. Automatic scoring of p ronunciation quality. Speech Communication 30 [ J ], 2000, 83-93. [ 3 ] S. M. W itt, S. J. Young. Phone2level p ronunciation scoring and assessment for interactive language learning [A ]. In: Speech Communication 30, 2000, 95-108. [ 4 ] S. M. W itt, U se of speech recognition in computer2assisted language learning, Doctor s D issertation of Cam2 bridge[d ], 1999. [ 5 ] C. Cucchiarini, F. D. W et, H. Strik, L. Boves. A ssessment of Dutch p ronunciation by means of automatic speech recognition technology[a ]. ICSLP, Vol. 5 [ C ], 1998, 1739-1742. [ 6 ] C. Cucchiarini, H. Strik, L. Boves. Automatic evaluation of dutch p ronunciation by using speech recognition technology[a ]. Proceedings of the IEEE workshop ASRU [ C ], Santa Barbara. 1997, 622-629. [ 7 ] A ijun L i, Xia W ang, A Contrastive Investigation of Standard Mandarin and Accented [A ]. EuroSpeech [ C ], 2003, 1139-1142. [ 8 ],,,. [A ]. [ C ], 2005, 22-25. [ 9 ],. [A ]. [ J ], 1998, 48-53. [ 10 ],,. [A ]. [ C ], 2005, 26-30. [ 11 ] C. J. Leggetter, P. C. Woodland, Maximum L ikelihood L inear Regression for Speaker Adap tation of Contin2 uous Density H idden M arkov Models, Computer Speech and Language[ J ], 1995, 171-185. 96