Abstract
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.
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Rank, E., Kubin, G. (2001). Nonlinear Synthesis of Vowels in the LP Residual Domain with a Regularized RBF Network. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_90
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DOI: https://doi.org/10.1007/3-540-45723-2_90
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