  Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral
coefficients as the vocal tract parameterization of the speech signal. Mel
Cepstral coefficients were never intended to work in a parametric speech
synthesis framework, but as yet, there has been little success in creating a
better parameterization that is more suited to synthesis. In this paper, we use
deep learning algorithms to investigate a data-driven parameterization
technique that is designed for the specific requirements of synthesis. We
create an invertible, low-dimensional, noise-robust encoding of the Mel Log
Spectrum by training a tapered Stacked Denoising Autoencoder (SDA). This SDA is
then unwrapped and used as the initialization for a Multi-Layer Perceptron
(MLP). The MLP is fine-tuned by training it to reconstruct the input at the
output layer. This MLP is then split down the middle to form encoding and
decoding networks. These networks produce a parameterization of the Mel Log
Spectrum that is intended to better fulfill the requirements of synthesis.
Results are reported for experiments conducted using this resulting
parameterization with the ClusterGen speech synthesizer.
