This paper presents a neural network based verification method in an HMM-based on-line Korean handwriting recognition system. It penalizes unreasonable grapheme hypotheses and complements global and structural information to the HMM-based recognition system, which is intrinsically based on local information. In the proposed system, each grapheme has one neural network verifier as well as one HM...

There are many shared attributes between existing iterative aligners and Hidden Markov Model (HMM). A learning algorithm of HMM called Viterbi is the same as the iteration of DP-matching of iterative aligners. HMM aligners can use the result of an iterative aligner initially, incorporate the similarity score of amino acids, and apply the detailed gap cost systems to improve the matching accurac...

We propose a new class of hidden Markov model (HMM) which we call Asynchronous-Transition HMM (AT-HMM) to model asynchronous temporal structure of acoustic feature sequences. Conventional HMM models a sequence of feature vectors, while temporally changing patterns of acoustic features do not necessarily synchronize with each other. In this paper, AT-HMMs with and without sequential constraints ...

This research investigated whether acoustic-phonetic knowledge of the mother tongue of a non-native speaker can be used to adapt an existing target language phoneme HMM recognizer. For this purpose three sets of phoneme HMMs were generated, one representing the target language (German), one the mother tongue of the non-native speaker (Turkish), and the third the foreign-accented pronunciation o...

A Hidden Markov Model (HMM) Toolbox within the Matlab environment is presented. In this toolbox, the conventional techniques for the continuous and discrete HMM are developed for the training as well as for the test phases. The ability to make different groups of components for the vector pattern is provided. Multilabeling techniques for the discrete HMM is also provided. The toolbox includes p...