Prototype-based discriminative training for various speech units
Erik McDermott, Shigeru Katagiri · 1992
It has since been shown that learning vector quantisation (LVQ) is a special case of a more general method, generalized probabilistic descent (GPD), for gradient descent on a rigorously defined classification loss measure that closely reflects the misclassification rate. The authors to extend LVQ into a prototype-based classifier appropriate for the classification of various long speech units. For word recognition, a dynamic time warping procedure is integrated into the GPD learning procedure. The resulting minimum error classifier (MEC) is no longer a purely LVQ-like method, and it is called the prototype-based minimum error classifier (PBMEC). Results for the difficult Bell Labs E-set task as well as for speaker-dependent isolated word recognition for a vocabulary of 5240 words are presented. They reveal clear gains in performance as a result of using PBMEC.>