Using LSTM neural networks for cross-lingual phonetic speech segmentation with an iterative correction procedure

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43969623" target="_blank" >RIV/49777513:23520/24:43969623 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/coin.12602" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/coin.12602</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/coin.12602" target="_blank" >10.1111/coin.12602</a>

Result language
angličtina
Original language name
Using LSTM neural networks for cross-lingual phonetic speech segmentation with an iterative correction procedure
Original language description
This article describes experiments on speech segmentation using long short-term memory recurrent neural networks. The main part of the paper deals with multi-lingual and cross-lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi-lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well-tuned hidden Markov model-based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text-to-speech system. The generated speech quality was compared with the reference segmentation by a preference listening test.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems

Project
<a href="/en/project/GA19-19324S" target="_blank" >GA19-19324S: Fully Trainable Deep Neural Network Based Czech Text-to-Speech Synthesis</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

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