Minimum Divergence Based Discriminative Training
- Jun Du ,
- Peng Liu ,
- Frank Soong ,
- Jian-Lai Zhou ,
- Ren-Hua Wang
Proc. of INTERSPEECH 2006 |
Published by International Speech Communication Association
We propose to use Minimum Divergence(MD) as a new measure of errors in discriminative training. To focus on improving discrimination between any two given acoustic models, we refine the error definition in terms of Kullback-Leibler Divergence (KLD) between them. The new measure can be regarded as a modified version of Minimum Phone Error (MPE) but with a higher resolution than just a symbol matching based criterion. Experimental recognition results show the new MD based training yields relative word error rate reductions of 57.8% and 6.1% on TIDigits and Switchboard databases, respectively, in comparing with the ML trained baseline systems. The recognition performance of MD is also shown to be consistently better than that of MPE.
© 2007 ISCA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ISCA and/or the author.