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In this paper, we present a synthesized stereo-based stochastic mapping approach for robust speech recognition. We extend the traditional stereo-based stochastic mapping (SSM) in two main aspects. First, the constraint of stereo-data, which is not practical in real applications, is relaxed by using HMM-based speech synthesis. Then we make feature mapping more focused on those incorrectly recognized...
This paper presents a new approach to compensating affine distortion of an isolated online handwritten Chinese character. The input sample is first analyzed by using a character-structure-guided orientation estimation approach. If necessary, the orientation hypotheses are refined based on confidence evaluation of two pre-classifiers. Depending on the number of possible orientations, an HMM-based minimax...
This paper presents a new approach to large-vocabulary online handwritten Chinese character recognition based on semi-tied covariance (STC) modeling. Detailed procedures are described for estimating the STC model parameters under both maximum likelihood (ML) and minimum classification error (MCE) criteria. Compared with the state-of-the-art modified quadratic discriminant function (MQDF) based classifiers,...
In this study, we propose a novel optimization algorithm for minimum classification error (MCE) training of modified quadratic discriminant function (MQDF) models. An ellipsoid constrained quadratic programming (ECQP) problem is formulated with an efficient line search solution derived, and a subspace combination condition is proposed to simplify the problem in certain cases. We show that under the...
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