  This paper proposes a deep denoising auto-encoder technique to extract better
acoustic features for speech synthesis. The technique allows us to
automatically extract low-dimensional features from high dimensional spectral
features in a non-linear, data-driven, unsupervised way. We compared the new
stochastic feature extractor with conventional mel-cepstral analysis in
analysis-by-synthesis and text-to-speech experiments. Our results confirm that
the proposed method increases the quality of synthetic speech in both
experiments.
