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The Bayes decision theory is the foundation of the classical statistical pattern recognition approach, with the expected error as the performance objective. For most pattern recognition problems, the “error” is conventionally assumed to be binary, i.e., 0 or 1, equivalent to error counting, independent of the specifics of the error made by the system. The term “error rate” is thus long considered...
The classical Bayes decision theory [3] is the foundation of statistical pattern recognition. In [4], we have addressed the issue of non-uniform error criteria in statistical pattern recognition, and generalized the Bayes decision theory for pattern recognition tasks where errors over different classes have varying degrees of significance. We further introduced the weighted minimum classification...
The classical Bayes decision theory [1] is the foundation of statistical pattern recognition. Conventional applications of the Bayes decision theory result in ubiquitous use of the maximum a posteriori probability (MAP) decision policy and the paradigm of distribution estimation as practice in the design of a statistical pattern recognition system. In this paper, we address the issue of non-uniform...
This paper presents an investigation of the rescoring performance using hidden Markov model (HMM) based attribute detectors. The minimum verification error (MVE) criterion is employed to enhance the reliability of the detectors in continuous speech recognition. The HMM-based detectors are applied on the possible recognition candidates, which are generated from the conventional decoder and organized...
The Bayes decision theory is the foundation of the classical statistical pattern recognition approach. For most of pattern recognition problems, the Bayes decision theory is employed assuming that the system performance metric is defined as the simple error counting, which assigns identical cost to each recognition error. However, this prevalent performance metric is not desirable in many practical...
Noise environment and natural spoken speech, is still a challenging issue for speech recognition. In this paper, study on this field is explored on Mandarin speech, from aspects of signal processing, acoustic model, language model, decoding algorithm, and post processing. The two-phase mel-warped wiener filter algorithm is improved for obtaining noise-robust feature. Segmentation algorithm and gender...
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