Laplacian

interaction of Linear Discriminant Analy-and a modeling approach using continuous mixture density HMMs is studied experi-mentally. The largest improvements in speech recog-nition accuracy could be obtained when the classes for the LDA transform were defined to be sub-phone units. On a 12,000-word German recognition task with small overlap between training and test vocabulary a reduction in error rate by one fifth was achieved com-pared to the case without LDA. On the development set of the DARPA RM1 task the error rate wqs reduced by one third. For the DARPA speaker-dependent no-grammar case, the error rate averaged over 12 speakers was 9.9%. This was achieved with a recognizer employ-ing LDA and a set of only 47 Viterbi-trained context-independent phonemes. 1