LSTM-based Speech Segmentation for TTS Synthesis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43955907" target="_blank" >RIV/49777513:23520/19:43955907 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-27947-9_31" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-27947-9_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-27947-9_31" target="_blank" >10.1007/978-3-030-27947-9_31</a>
Alternative languages
Result language
angličtina
Original language name
LSTM-based Speech Segmentation for TTS Synthesis
Original language description
This paper describes experiments on speech segmentation for the purposes of text-to-speech synthesis. We used a bidirectional LSTM neural network for framewise phone classification and another bidirectional LSTM network for predicting the duration of particular phones. The proposed segmentation procedure combines both outputs and finds the optimal speech-phoneme alignment by using the dynamic programming approach. We introduced two modifications to increase the robustness of phoneme classification. Experiments were performed on 2 professional voices and 2 amateur voices. A comparison with a reference HMM-based segmentation with additional manual corrections was performed. Preference listening tests showed that the reference and experimental segmentation are equivalent when used in a unit selection TTS system.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Text, Speech, and Dialogue 22nd International Conference, TSD 2019, Ljubljana,Slovenia, September 11-13, 2019, Proceedings
ISBN
978-3-030-27946-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
361-372
Publisher name
Springer
Place of publication
Cham
Event location
Ljubljana, Slovenia
Event date
Sep 11, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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