“…Motivated by applications of the Viterbi training (VT) algorithm to estimate parameters of hidden Markov models (HMM) in speech recognition (Huang, Ariki, and Jack [11], Ney, Steinbiss, Haeb-Umbach, Tran, and Essen [26], Rabiner and Juang [31], Rabiner, Wilpon, and Juang [32], Steinbiss et al [36], Ström, Hetherington, Hazen, Sandness, and Glass [37]), natural language models (Ji and Bilmes [14], Och and Ney [27]), image analysis (Joshi, Li, and Wang [15], Li, Gray, and Olshen [22]), bioinformatics (Ehret et al [8], Ohler, Niemann, Liao, and Rubin [28]), and gene discovery via unsupervized learning (Lomsadze, Ter-Hovhannisyan, Chernoff, and Borodovsky [24]), we propose a new principled way to improve the accuracy of the VT estimators while preserving the essential computational advantages of the baseline algorithm.…”