Comparison of discriminative training criteria and optimization methods for speech recognition
نویسندگان
چکیده
منابع مشابه
Comparison of discriminative training criteria and optimization methods for speech recognition
The aim of this work is to build up a common framework for a class of discriminative training criteria and optimization methods for continuous speech recognition. A uni®ed discriminative criterion based on likelihood ratios of correct and competing models with optional smoothing is presented. The uni®ed criterion leads to particular criteria through the choice of competing word sequences and th...
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In this work we compare two parameter optimization techniques for discriminative training using the MMI criterion: the extended Baum-Welch (EBW) algorithm and the generalized probabilistic descent (GPD) method. Using Gaussian emission densities we found special expressions for the step sizes in GPD, leading to reestimation formula very similar to those derived for the EBW algorithm. Results wer...
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In this paper, a formally unifying approach for a class of discriminative training criteria including Maximum Mutual Information (MMI) and Minimum Classification Error (MCE) criterion is presented, including the optimization methods gradient descent (GD) and extended Baum-Welch (EB) algorithm. Comparisons are discussed for the MMI and the MCE criterion, including the determination of the sets o...
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In speech recognition, discriminative training has proved to be an effective method to improve recognition accuracy. It has successfully improved systems of different scales and different languages. While discriminative training has been developing for over 20 years, it continues to draw attention to researchers and remains to be one of the most important topics in speech recognition to date. D...
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ژورنال
عنوان ژورنال: Speech Communication
سال: 2001
ISSN: 0167-6393
DOI: 10.1016/s0167-6393(00)00035-2