Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Denoising Auto-encoder for Statistical Speech Synthesis   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.

22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con