Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: vTTS: visual-text to speech   This paper proposes visual-text to speech (vTTS), a method for synthesizing
speech from visual text (i.e., text as an image). Conventional TTS converts
phonemes or characters into discrete symbols and synthesizes a speech waveform
from them, thus losing the visual features that the characters essentially
have. Therefore, our method synthesizes speech not from discrete symbols but
from visual text. The proposed vTTS extracts visual features with a
convolutional neural network and then generates acoustic features with a
non-autoregressive model inspired by FastSpeech2. Experimental results show
that 1) vTTS is capable of generating speech with naturalness comparable to or
better than a conventional TTS, 2) it can transfer emphasis and emotion
attributes in visual text to speech without additional labels and
architectures, and 3) it can synthesize more natural and intelligible speech
from unseen and rare characters than conventional TTS.

27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
2
The categories of this paper are: math
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex