# Phonological Features for 0-shot Multilingual Speech Synthesis

arXiv: https://arxiv.org/abs/2008.04107 · HTML: https://mlsanity.com/paper/2008.04107/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

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

- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis](https://mlsanity.com/paper/2103.03541/) (2021)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining](https://mlsanity.com/paper/2301.12596/) (2023)
- [Cross-lingual Low Resource Speaker Adaptation Using Phonological Features](https://mlsanity.com/paper/2111.09075/) (2021)
- [Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)](https://mlsanity.com/paper/2207.01547/) (2022)
- [Revisiting IPA-based Cross-lingual Text-to-speech](https://mlsanity.com/paper/2110.07187/) (2021)
- [Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats](https://mlsanity.com/paper/2405.04271/) (2024)
- [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)
- [MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery](https://mlsanity.com/paper/2512.19612/) (2025)
- [Applying Phonological Features in Multilingual Text-To-Speech](https://mlsanity.com/paper/2110.03609/) (2021)
- [Data-augmented cross-lingual synthesis in a teacher-student framework](https://mlsanity.com/paper/2204.00061/) (2022)
- [Integrating Human Linguistic Insights into AI: Theory-Driven Representation for Multilingual Text-to-Speech](https://mlsanity.com/paper/2204.07228/) (2022)
- [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)
- [PRESENT: Zero-Shot Text-to-Prosody Control](https://mlsanity.com/paper/2408.06827/) (2024)
- [CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation](https://mlsanity.com/paper/2412.20048/) (2024)

## Abstract (arXiv)

Code-switching---the intra-utterance use of multiple languages---is prevalent across the world. Within text-to-speech (TTS), multilingual models have been found to enable code-switching. By modifying the linguistic input to sequence-to-sequence TTS, we show that code-switching is possible for languages unseen during training, even within monolingual models. We use a small set of phonological features derived from the International Phonetic Alphabet (IPA), such as vowel height and frontness, consonant place and manner. This allows the model topology to stay unchanged for different languages, and enables new, previously unseen feature combinations to be interpreted by the model. We show that this allows us to generate intelligible, code-switched speech in a new language at test time, including the approximation of sounds never seen in training.