CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Unaligned Training for Voice Conversion based on a Local-nonlinear Principal Component Analysis Approach

Behrooz Makki (Institutionen för signaler och system, Kommunikationssystem) ; Seyedali Seyedsalehi ; Mona Noori Hosseini ; Nasser Sadati
Neural Computing and Applications (0941-0643). Vol. 19 (2009), 3, p. 437-444.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Local nonlinear principal component analysis; Unaligned voice conversion; Autoassociative neural network

Denna post skapades 2009-02-26. Senast ändrad 2013-10-29.
CPL Pubid: 90444


Läs direkt!

Länk till annan sajt (kan kräva inloggning)

Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem


Elektroteknik och elektronik

Chalmers infrastruktur