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- W2111998032 abstract "This letter demonstrates hidden Markov model (HMM), multilayer perceptron (MLP), and time-delay recursive neural network (TDRNN) architectures for the purpose of recognizing pitch accents given observation of the F0 and energy trajectories. At an insertion error rate of 25%, the deletion error rates of the MLP, TDRNN, and HMM are 13.2%, 7.9%, and 32.7%, respectively, despite the fact that both MLP and TDRNN have 70% fewer trainable parameters than the HMM. Error analysis suggests that low-pitch accents may require long-term context to correctly recognize, while high-pitch accents may be recognizable based on local pitch contour." @default.
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- W2111998032 date "2004-07-01" @default.
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- W2111998032 title "Automatic Recognition of Pitch Movements Using Multilayer Perceptron and Time-Delay Recursive Neural Network" @default.
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- W2111998032 doi "https://doi.org/10.1109/lsp.2004.830114" @default.
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