Meta Learning Text-to-Speech Synthesis in over 7000 Languages
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Abstract (arXiv)
In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development. By leveraging a novel integration of massively multilingual pretraining and meta learning to approximate language representations, our approach enables zero-shot speech synthesis in languages without any available data. We validate our system's performance through objective measures and human evaluation across a diverse linguistic landscape. By releasing our code and models publicly, we aim to empower communities with limited linguistic resources and foster further innovation in the field of speech technology.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 10 Jun 2024.
Builds on
33 of 50 references.
References · 33
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- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
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- 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
- 2023Scaling Speech Technology to 1,000+ Languagescited by 401
- 2023Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervisioncited by 167
- 2020FastPitch: Parallel Text-to-speech with Pitch Predictioncited by 150
- 2020AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselinescited by 111
- 2024Proactive Detection of Voice Cloning with Localized Watermarkingcited by 67
21 more
- 2021Hi-Fi Multi-Speaker English TTS Datasetcited by 60
- 2018Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytescited by 54
- 2019CSS10: A Collection of Single Speaker Speech Datasets for 10 Languagescited by 54
- 2022HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancementcited by 49
- 2021PortaSpeech: Portable and High-Quality Generative Text-to-Speechcited by 47
- 2020LRSpeech: Extremely Low-Resource Speech Synthesis and Recognitioncited by 43
- 2019End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learningcited by 30
- 2019ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detectioncited by 21
- 2022Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Featurescited by 20
- 2023Zambezi Voice: A Multilingual Speech Corpus for Zambian Languagescited by 18
- 2022Low-Resource Multilingual and Zero-Shot Multispeaker TTScited by 15
- 2022Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speechcited by 15
- 2021HUI-Audio-Corpus-German: A high quality TTS datasetcited by 14
- 2022Building African Voicescited by 14
- 2023ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpuscited by 14
- 2022Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speechcited by 13
- 2021Cross-lingual Transfer for Speech Processing using Acoustic Language Similaritycited by 8
- 2023The IMS Toucan System for the Blizzard Challenge 2023cited by 6
- 2023Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selectioncited by 2
- 2022Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languagescited by 1
- 2023The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languagescited by 1
Built on by
Citing papers per year: 2024: 2; 2025: 5; 2026: 2.
9 papers.
- 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Aheadcited by 15
- 2025Investigating Stochastic Methods for Prosody Modeling in Speech Synthesiscited by 2
- 2024Can DeepFake Speech be Reliably Detected?cited by 1
- 2025What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detectioncited by 1
- 2026OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languagescited by 1
- 2024Everyday Speech in the Indian Subcontinentcited by 0
- 2025Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesiscited by 0
- 2025Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languagescited by 0
- 2026Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpuscited by 0
- 2026OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages8 Jun 2026
- 2026Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus13 Apr 2026
- 2025Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis30 Jun 2025
- 2025Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages4 Jun 2025
- 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead27 May 2025
- 2025What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection23 May 2025
- 2025Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesis10 Apr 2025
- 2024Everyday Speech in the Indian Subcontinent14 Oct 2024
- 2024Can DeepFake Speech be Reliably Detected?9 Oct 2024