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- W29127967 abstract "We propose a parameter shrinkage adaptation framework to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing an objective function with a sum of parameterwise power q constraint. For the first attempt, we formulate ridge maximum likelihood linear regression (MLLR) and ridge constraint MLLR (CMLLR) with an element-wise square sum constraint to regularize the objective functions of the conventional MLLR and CMLLR, respectively. Tested on the 5k-WSJ0 task, the proposed ridge MLLR and ridge CMLLR algorithms give significant word error rate reduction from the errors obtained with standard MLLR and CMLLR in an utterance-by-utterance unsupervised adaptation scenario." @default.
- W29127967 created "2016-06-24" @default.
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- W29127967 date "2010-09-26" @default.
- W29127967 modified "2023-09-25" @default.
- W29127967 title "Shrinkage model adaptation in automatic speech recognition" @default.
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- W29127967 doi "https://doi.org/10.21437/interspeech.2010-478" @default.
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