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• W43454013 abstract "We perform large margin training of HMM acoustic parameters by maximizing a penalty function which combines two terms. The first term is a scale which gets multiplied with the Hamming distance between HMM state sequences to form a multi-label (or sequence) margin. The second term arises from constraints on the training data that the joint log-likelihoods of acoustic and correct word sequences exceed the joint log-likelihoods of acoustic and incorrect word sequences by at least the multi-label margin between the corresponding Viterbi state sequences. Using the softmax trick, we collapse these constraints into a boosted MMI-like term. The resulting objective function can be efficiently maximized using extended Baum-Welch updates. Experimental results on multiple LVCSR tasks show a good correlation between the objective function and the word error rate." @default.
• W43454013 created "2016-06-24" @default.
• W43454013 creator A5079994647 @default.
• W43454013 creator A5084286453 @default.
• W43454013 date "2008-09-22" @default.
• W43454013 modified "2023-09-26" @default.
• W43454013 title "Penalty function maximization for large margin HMM training" @default.
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• W43454013 doi "https://doi.org/10.21437/interspeech.2008-108" @default.
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