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• W2145823193 abstract "We show a number of improvements in the use of Hidden Conditional Random Fields (HCRFs) for phone classification on the TIMIT and Switchboard corpora. We first show that the use of regularization effectively prevents overfitting, improving over other methods such as early stopping. We then show that HCRFs are able to make use of non-independent features in phone classification, at least with small numbers of mixture components, while HMMs degrade due to their strong independence assumptions. Finally, we successfully apply Maximum a Posteriori adaptation to HCRFs, decreasing the phone classification error rate in the Switchboard corpus by around 1% – 5% given only small amounts of adaptation data." @default.
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• W2145823193 date "2007-01-01" @default.
• W2145823193 modified "2023-09-25" @default.
• W2145823193 title "Regularization, adaptation, and non-independent features improve hidden conditional random fields for phone classification" @default.
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• W2145823193 doi "https://doi.org/10.1109/asru.2007.4430136" @default.
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