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In our previous work, a precision constrained Gaussian model (PCGM) was proposed for character modeling to design compact recognizers of handwritten Chinese characters. A maximum likelihood training procedure was developed to estimate model parameters from training data. In this paper, we extend the above work by using minimum classification error (MCE) training to improve recognition accuracy and...
In this paper we propose a generalized feature transformation approach to compensating for channel variation in speaker verification (SV) applications. Channel-dependent (CD) piecewise linear transformations are used for feature compensation. CD transformation parameters are estimated together with a channel-independent (CI) root Gaussian mixture model (GMM) from training data with a variety of channel...
In this paper, we present a formulation of minimum classification error linear regression (MCELR) for the adaptation of Gaussian mixture continuous-density hidden Markov model (CDHMM) parameters. Two optimization approaches, namely generalized probabilistic descent (GPD) and Quickprop are studied and compared for the optimization of the MCELR objective function. The effectiveness of the proposed MCELR...
In our previous works, a segmental switching linear Gaussian hidden Markov model (SSLGHMM) was proposed to model "noisy" speech utterance for robust speech recognition. Both ML (maximum likelihood) and MCE (minimum classification error) training procedures were developed for training model parameters and their effectiveness was confirmed by evaluation experiments on Aurora2 and Aurora3 databases...
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