Neural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns

Küçük Resim Yok

Tarih

2003

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

PERGAMON-ELSEVIER SCIENCE LTD

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Input data representation is highly decisive in neural learning in terms of convergence. In this paper, within an analytical and statistical framework, the effect of the distribution characteristics of the input pattern vectors on the performance of the back-propagation (BP) algorithm is established for a function approximation problem, where parameters of an articulatory speech synthesizer are estimated from acoustic input data. The aim is to determine the optimum statistical characteristics of the acoustic input patterns in order to improve neural learning. Improvement is obtained through a modification of the statistical characteristics of the input data, which reduces effectively the occurrence of node saturation in the hidden layer. (C) 2002 Elsevier Science Ltd. All rights reserved.

Açıklama

Anahtar Kelimeler

back-propagation algorithm, statistical dependency of neural learning, articulatory speech synthesizer

Kaynak

COMPUTERS & ELECTRICAL ENGINEERING

WoS Q Değeri

Q4

Scopus Q Değeri

Q1

Cilt

29

Sayı

6

Künye