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• W2169726209 abstract "In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. It is well known that such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. We describe a numerical method, the Metropolis-Hastings (M-H) algorithm, which provides a Bayes estimator for the distribution. We experimentally evaluate this algorithm for a speaker recognition task, demonstrating a fivefold reduction in root mean squared error." @default.
• W2169726209 created "2016-06-24" @default.
• W2169726209 creator A5051646883 @default.
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• W2169726209 date "2008-05-01" @default.
• W2169726209 modified "2023-10-18" @default.
• W2169726209 title "Towards Link Characterization From Content: Recovering Distributions From Classifier Output" @default.
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• W2169726209 doi "https://doi.org/10.1109/tasl.2008.920060" @default.
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