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International Journal of Advanced Research
Article . 2018 . Peer-reviewed
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ZENODO
Article . 2018
License: CC BY
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ZENODO
Article . 2018
License: CC BY
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ZENODO
Article . 2018
License: CC BY
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SPEECH RECOGNITION USING THE EMPIRICAL MODE DECOMPOSITION METHOD.

Authors: M. S. Medvedev.;

SPEECH RECOGNITION USING THE EMPIRICAL MODE DECOMPOSITION METHOD.

Abstract

In this paper the using of empirical mode decomposition for creation of the Russian phoneme models in a system of speech-to-text conversion is considered. The proposed method is compared with the Fourier transform and wavelet transform. The experimental evaluation has shown that this method has advantages in the task of speech features formation (within neural network approach).

Related Organizations
Keywords

Speech recognition; Hilbert-Huang transform; wavelet analysis; empirical mode decomposition.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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gold