Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Multilingual Frontend for TTS   This paper describes progress towards making a Neural Text-to-Speech (TTS)
Frontend that works for many languages and can be easily extended to new
languages. We take a Machine Translation (MT) inspired approach to constructing
the frontend, and model both text normalization and pronunciation on a sentence
level by building and using sequence-to-sequence (S2S) models. We experimented
with training normalization and pronunciation as separate S2S models and with
training a single S2S model combining both functions.
  For our language-independent approach to pronunciation we do not use a
lexicon. Instead all pronunciations, including context-based pronunciations,
are captured in the S2S model. We also present a language-independent chunking
and splicing technique that allows us to process arbitrary-length sentences.
Models for 18 languages were trained and evaluated. Many of the accuracy
measurements are above 99%. We also evaluated the models in the context of
end-to-end synthesis against our current production system.

33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
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The categories of this paper are: eess
24
The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
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The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol