The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform
Open accessAbstract
We present a new workflow to create components for the MaryTTS text-to-speech synthesis platform, which is popular with researchers and developers, extending it to support new languages and custom synthetic voices. This workflow replaces the previous toolkit with an efficient, flexible process that leverages modern build automation and cloud-hosted infrastructure. Moreover, it is compatible with the updated MaryTTS architecture, enabling new features and state-of-the-art paradigms such as synthesis based on deep neural networks (DNNs). Like MaryTTS itself, the new tools are free, open source software (FOSS), and promote the use of open data.
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In the citing paper
“In addition to legacy TTS and VC algorithms (e.g., MaryTTS [16]), ASVspoof 5 spoofing attacks were generated using the latest DNN-based methods (e.g., ZMMTTS [17]).”
From ASVspoof 5: crowdsourced speech data, deepfakes, and adversarial attacks at scale (2024) · cites this paper as [16]
In the citing paper
“A19 is an attack using MaryTTS [25]”
From Robust audio deepfake detection: exploring front-/back-end combinations and data augmentation strategies for the ASVspoof5 Challenge (2024) · cites this paper as [25]
Citations
Cites
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TensorFlow: A system for large-scale machine learningPreprint
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Kaldi Speech Recognition Toolkit
Povey, D. (2024) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.57702/jb3fvbn9 · Cited by 5,270
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
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Morise, M., Yokomori, F., & Ozawa, K. (2016) · IEICE Trans. Inf. Syst. · doi:10.1587/transinf.2015edp7457 · Cited by 1,228
Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi
McAuliffe, M., Socolof, M., Mihuc, S., Wagner, M., & Sonderegger, M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1386 · Cited by 1,169
Sphinx-4: a flexible open source framework for speech recognition
Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Gouvêa, E. B., Wolf, P., & Woelfel, J. (2004) · Cited by 417
The German Text-to-Speech Synthesis System MARY: A Tool for Research, Development and Teaching
Schröder, M., & Trouvain, J. (2003) · International Journal of Speech Technology · doi:10.1023/a:1025708916924 · Cited by 382
Merlin: An Open Source Neural Network Speech Synthesis System
Wu, Z., Watts, O., & King, S. (2016) · doi:10.21437/ssw.2016-33 · Cited by 304
Cited by
Sorted by citations
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505
Self-citationASVspoof 5: crowdsourced speech data, deepfakes, and adversarial attacks at scale
Wang, X., Delgado, H., Tak, H., Jung, J.-W., Shim, H.-J., Todisco, M., Kukanov, I., Liu, X., Sahidullah, M., Kinnunen, T. H., et al. (2024) · The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024) · doi:10.21437/asvspoof.2024-1 · Cited by 101
Phonetic accommodation in interaction with a virtual language learning tutor: A Wizard-of-Oz study
Gessinger, I., Möbius, B., Maguer, S. L., Raveh, E., & Steiner, I. (2021) · Journal of Phonetics · doi:10.1016/j.wocn.2021.101029 · Cited by 35
Self-citationWang, X., Delgado, H., Tak, H., Jung, J.-W., Shim, H.-J., Todisco, M., Kukanov, I., Liu, X., Sahidullah, M., Kinnunen, T., et al. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2025.101825 · Cited by 30
“A10 : identical to A09 , except for the prediction of pitch and energy from the input text using CNNs [45] and the use of a smaller HiFi-GAN vocoder model [58] than the A07 attack upon which A09 is based.”
The limits of the Mean Opinion Score for speech synthesis evaluation
Maguer, S. L., King, S., & Harte, N. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101577 · Cited by 23
Schäfer, K., Choi, J.-E., & Neu, M. (2024) · The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024) · doi:10.21437/asvspoof.2024-9 · Cited by 10
“A19 is an attack using MaryTTS [25], A20 and A30 are adversarial attacks [24].”
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2019) · Graduate School and Research Center in Digital Science (EURECOM) · Cited by 3
Self-citationLiaison and Pronunciation Learning in End-to-End Text-to-Speech in French
Taylor, J., Maguer, S. L., & Richmond, K. (2021) · ISCA Speech Synthesis Workshop (SSW) · doi:10.21437/ssw.2021-34 · Cited by 2
Can Auditory Nerve Models Tell us What’s Different About WaveNet Vocoded Speech?
Maguer, S. L., & Harte, N. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2596 · Cited by 1
Investigation of Auditory Nerve Model Based Analysis for Vocoded Speech Synthesis
Maguer, S. L., & Harte, N. (2020) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex48832.2020.9123101 · Cited by 1
Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children
Lakmal, M. A. J. A., Methmini, K. A. D. G., Rupasinghe, D., Hettiarachchi, D. I., Piyawardana, V., Senarathna, M., Reyal, S., & Pulasinghe, K. (2021) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr54349.2021.9657394 · Cited by 1
Synthesising Expressive Speech – Which Synthesiser for VOCAs?
Wülfing, J.-O., Dang, C. T., & André, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58323-1_43
Step-by-Step Process of Building Voices for Under Resourced Languages using MARY TTS Platform
Senarathna, M., Pulasinghe, K., & Reyal, S. (2022) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac57685.2022.10025200
Metadata
- Authors
- Steiner, I., & Le Maguer, S.
- Affiliations
- Saarland University (DE)
- ORCID
- Steiner, Ingmar
- Venue
- Proceedings of the Language Resources and Evaluation Conference
- Published
- 2018-05
- ISSN
- 2522-2686
- OpenAlex
- W2774128158
- Cited by
- 11
- References
- 17
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 1
- FWCI
- 0.81
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Language Resources and Evaluation Conference (LREC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Language Resources and Evaluation
- Funders
- Deutsche Forschungsgemeinschaft (SFB 1102, EXC 284)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- text-to-speech, synthetic speech, language support, build automation, cloud infrastructure, open source software, open data
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