Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Linear Prediction of Speech
Nonlinear dynamic modeling of the voiced excitation for improved speech synthesis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Application of Bayesian trained RBF networks to nonlinear time-series modeling
Signal Processing - From signal processing theory to implementation
Investigation on LP-residual representations for speaker identification
Pattern Recognition
Identification of nonlinear oscillator models for speech analysis and synthesis
Nonlinear Speech Modeling and Applications
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In this paper we present a speech analysis/synthesis coder based on a combination of linear prediction with nonlinear modeling of the residual using a regularized radial basis function (RBF) network. The model has been applied to synthesis of sustained vowel signals and has been found to preserve the dynamics and spectra of the original speech signal. While several nonlinear speech models reportedly suffer from high-frequency losses in the synthesized speech due to system inherent low-pass behavior, our approach achieves good speech signal reproduction even in the higher frequency ranges. The decomposition of the speech signal by linear prediction analysis supports processing during synthesis such as pitch modifications while the nonlinear modeling provides the means for adequate reproduction of the fine-grained dynamic characteristics of speech.