@inproceedings{gunduz-etal-2024-automated-end,
title = "An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation",
author = "Gunduz, Ahmet and
Yuksel, Kamer Ali and
Darwish, Kareem and
Javadi, Golara and
Minazzi, Fabio and
Sobieski, Nicola and
Brati{\`e}res, S{\'e}bastien",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.93",
pages = "1043--1051",
abstract = "Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies.",
}
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<abstract>Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies.</abstract>
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%0 Conference Proceedings
%T An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation
%A Gunduz, Ahmet
%A Yuksel, Kamer Ali
%A Darwish, Kareem
%A Javadi, Golara
%A Minazzi, Fabio
%A Sobieski, Nicola
%A Bratières, Sébastien
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F gunduz-etal-2024-automated-end
%X Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies.
%U https://aclanthology.org/2024.lrec-main.93
%P 1043-1051
Markdown (Informal)
[An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation](https://aclanthology.org/2024.lrec-main.93) (Gunduz et al., LREC-COLING 2024)
ACL
- Ahmet Gunduz, Kamer Ali Yuksel, Kareem Darwish, Golara Javadi, Fabio Minazzi, Nicola Sobieski, and Sébastien Bratières. 2024. An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1043–1051, Torino, Italia. ELRA and ICCL.