Sentences vs Phrases in Neural Speech Synthesis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973179" target="_blank" >RIV/49777513:23520/24:43973179 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-70566-3_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-70566-3_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70566-3_4" target="_blank" >10.1007/978-3-031-70566-3_4</a>

Result language
angličtina
Original language name
Sentences vs Phrases in Neural Speech Synthesis
Original language description
The neural network-based TTS models are usually trained and inferred on the whole sentences, or, in general, on longer chunks of speech. However, these may negatively affect the responsiveness of the TTS system in cases when latency should be kept as small as possible. We present experiments using smaller chunk lengths, namely phrases, and their impact on speech quality when various chunk length combinations are used for training and inference in the VITS synthesizer.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA22-27800S" target="_blank" >GA22-27800S: Transformers of multiple modalities for more natural spoken dialog</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Text, Speech, and Dialogue. Lecture Notes in Computer Science
ISBN
978-3-031-70565-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
10
Pages from-to
36-45
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Brno, Czech Republic
Event date
Sep 9, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001307848400004

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