Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Karaoker: Alignment-free singing voice synthesis with speech training
  data   Existing singing voice synthesis models (SVS) are usually trained on singing
data and depend on either error-prone time-alignment and duration features or
explicit music score information. In this paper, we propose Karaoker, a
multispeaker Tacotron-based model conditioned on voice characteristic features
that is trained exclusively on spoken data without requiring time-alignments.
Karaoker synthesizes singing voice following a multi-dimensional template
extracted from a source waveform of an unseen speaker/singer. The model is
jointly conditioned with a single deep convolutional encoder on continuous data
including pitch, intensity, harmonicity, formants, cepstral peak prominence and
octaves. We extend the text-to-speech training objective with feature
reconstruction, classification and speaker identification tasks that guide the
model to an accurate result. Except for multi-tasking, we also employ a
Wasserstein GAN training scheme as well as new losses on the acoustic model's
output to further refine the quality of the model.

7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc