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Neural Computing and Applications

Erschienen in:

01.04.2010 | Original Article

Unaligned training for voice conversion based on a local nonlinear principal component analysis approach

verfasst von: Behrooz Makki, Mona Noori Hosseini, Seyyed Ali Seyyedsalehi, Nasser Sadati

Erschienen in: Neural Computing and Applications | Ausgabe 3/2010

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Abstract

During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC.

Vorheriger Artikel Vehicle inductive signatures recognition using a Madaline neural network

Nächster Artikel A unified mathematical form for removing neurons based on orthogonal projection and crosswise propagation

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Titel: Unaligned training for voice conversion based on a local nonlinear principal component analysis approach
verfasst von: Behrooz Makki
Mona Noori Hosseini
Seyyed Ali Seyyedsalehi
Nasser Sadati
Publikationsdatum: 01.04.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0275-x

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