# Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

arXiv: https://arxiv.org/abs/2203.03191 · HTML: https://mlsanity.com/paper/2203.03191/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 68 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://mlsanity.com/paper/1811.00002/) (2018)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [Deep Voice: Real-time Neural Text-to-Speech](https://mlsanity.com/paper/1702.07825/) (2017)
- [End-to-End Adversarial Text-to-Speech](https://mlsanity.com/paper/2006.03575/) (2020)
- [ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit](https://mlsanity.com/paper/1910.10909/) (2019)
- [Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention](https://mlsanity.com/paper/1710.08969/) (2017)
- [Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes](https://mlsanity.com/paper/1811.09021/) (2018)
- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://mlsanity.com/paper/1903.11269/) (2019)
- [One TTS Alignment To Rule Them All](https://mlsanity.com/paper/2108.10447/) (2021)
- [LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition](https://mlsanity.com/paper/2008.03687/) (2020)

## Built on by (most cited first, 20 of 20)

- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation](https://mlsanity.com/paper/2312.02512/) (2023)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [Speaker Anonymization with Phonetic Intermediate Representations](https://mlsanity.com/paper/2207.04834/) (2022)
- [Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining](https://mlsanity.com/paper/2301.12596/) (2023)
- [MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning](https://mlsanity.com/paper/2405.11446/) (2024)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [The IMS Toucan System for the Blizzard Challenge 2023](https://mlsanity.com/paper/2310.17499/) (2023)
- [PoeticTTS -- Controllable Poetry Reading for Literary Studies](https://mlsanity.com/paper/2207.05549/) (2022)
- [Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats](https://mlsanity.com/paper/2405.04271/) (2024)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [CrossSpeech: Speaker-independent Acoustic Representation for Cross-lingual Speech Synthesis](https://mlsanity.com/paper/2302.14370/) (2023)
- [Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection](https://mlsanity.com/paper/2306.12040/) (2023)
- [Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis](https://mlsanity.com/paper/2507.00227/) (2025)
- [Controllable Generation of Artificial Speaker Embeddings through Discovery of Principal Directions](https://mlsanity.com/paper/2310.17502/) (2023)
- [HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks](https://mlsanity.com/paper/2404.04645/) (2024)
- [The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech](https://mlsanity.com/paper/2306.00535/) (2023)
- [CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation](https://mlsanity.com/paper/2412.20048/) (2024)
- [Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech](https://mlsanity.com/paper/2609.11545/) (2026)

## Abstract (arXiv)

While neural text-to-speech systems perform remarkably well in high-resource scenarios, they cannot be applied to the majority of the over 6,000 spoken languages in the world due to a lack of appropriate training data. In this work, we use embeddings derived from articulatory vectors rather than embeddings derived from phoneme identities to learn phoneme representations that hold across languages. In conjunction with language agnostic meta learning, this enables us to fine-tune a high-quality text-to-speech model on just 30 minutes of data in a previously unseen language spoken by a previously unseen speaker.