Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Cross-Domain Speech-to-Speech Conversion with
  Time-Frequency Consistency   In recent years generative adversarial network (GAN) based models have been
successfully applied for unsupervised speech-to-speech conversion.The rich
compact harmonic view of the magnitude spectrogram is considered a suitable
choice for training these models with audio data. To reconstruct the speech
signal first a magnitude spectrogram is generated by the neural network, which
is then utilized by methods like the Griffin-Lim algorithm to reconstruct a
phase spectrogram. This procedure bears the problem that the generated
magnitude spectrogram may not be consistent, which is required for finding a
phase such that the full spectrogram has a natural-sounding speech waveform. In
this work, we approach this problem by proposing a condition encouraging
spectrogram consistency during the adversarial training procedure. We
demonstrate our approach on the task of translating the voice of a male speaker
to that of a female speaker, and vice versa. Our experimental results on the
Librispeech corpus show that the model trained with the TF consistency provides
a perceptually better quality of speech-to-speech conversion.

29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph