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- W7958345 abstract "This paper describes a recurrent neural network (RNN) based phoneme recognition method incorporating articulatory dynamic parameters (Δ and ΔΔ). The method comprises three stages: (i) DPFs extraction using a recurrent neural network (RNN) from acoustic features, (ii) incorporation of dynamic parameters into a multilayer neural network (MLN) for reducing DPF context, and (iii) addition of an Inhibition/Enhancement (In/En) network for categorizing the DPF movement more accurately and Gram-Schmidt orthogonalization procedure for decorrelating the inhibited/enhanced data vector before connecting with a hidden Markov models (HMMs)-based classifier. From the experiments on Japanese Newspaper Article Sentences (JNAS), it is observed that the proposed method provides a higher phoneme correct rate over the method that does not incorporate dynamic articulatory parameters. Moreover, it reduces mixture components in HMM for obtaining a higher performance." @default.
- W7958345 created "2016-06-24" @default.
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- W7958345 date "2011-01-01" @default.
- W7958345 modified "2023-09-24" @default.
- W7958345 title "Recurrent Neural Network Based Phoneme Recognition Incorporating Articulatory Dynamic Parameters" @default.
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- W7958345 doi "https://doi.org/10.1007/978-3-642-22720-2_36" @default.
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