Title :
A data-driven method for input feature selection within neural prosody generation
Author :
Çağlayan Erdem;Hans Georg Zimmermann
Author_Institution :
Dresden University of Technology, D-01062, Germany
fDate :
5/1/2002 12:00:00 AM
Abstract :
The analysis and selection of input features within machine learning techniques is an important problem if a new system has to be established or the system has to be trained for a new task. Within a Text-to-Speech (ITS) application this task has to be handled while adapting a system to a new language or a new speaker.
Keywords :
"Artificial neural networks","Adaptation model","Data structures","Boolean functions","Training"
Conference_Titel :
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on
Print_ISBN :
0-7803-7402-9
DOI :
10.1109/ICASSP.2002.5743758
