Regularization, Adaptation, and Non-Independent Features Improve Hidden Conditional Random Fields for Phone Classification
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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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.
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