Segmental GPD training of HMM based speech recognizer

A novel training algorithm, segmental GPD (generalized probabilistic descent) training, for a hidden Markov model (HMM)-based speech recognizer using Viterbi decoding is proposed. This algorithm is based on the principle of minimum recognition error rate in which segmentation and discriminative training are jointly optimized. Various issues related to the special structure of HMM in segmental GPD training are studied. The authors tested this algorithm on two speaker-independent recognition tasks. The first experiment involves English E-set. Segmental GPD training was directly applied to HMM generated from nonoptimal uniform segmentation. A recognition rate of 88.7% was achieved on English E-set with whole word HMM. The second experiment involves the connected digits TI-database. Segmental GPD training was applied to HMM which were already trained using conventional training methods. A string recognition rate of 98.8% was achieved on 10-state word based HMM through segmental GPD training