# Phonikud: Overcoming Phonetic Underspecification for Hebrew Text-To-Speech

arXiv: https://arxiv.org/abs/2506.12311 · HTML: https://mlsanity.com/paper/2506.12311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models](https://mlsanity.com/paper/2306.07691/) (2023)
- [ByT5 model for massively multilingual grapheme-to-phoneme conversion](https://mlsanity.com/paper/2204.03067/) (2022)
- [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://mlsanity.com/paper/2308.16687/) (2023)
- [Restoring Hebrew Diacritics Without a Dictionary](https://mlsanity.com/paper/2105.05209/) (2021)
- [HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing](https://mlsanity.com/paper/2407.07566/) (2024)
- [A Language Modeling Approach to Diacritic-Free Hebrew TTS](https://mlsanity.com/paper/2407.12206/) (2024)
- [Enhancing TTS Stability in Hebrew using Discrete Semantic Units](https://mlsanity.com/paper/2410.21502/) (2024)

## Abstract (arXiv)

Text-to-speech (TTS) for Modern Hebrew is challenged by the language's orthographic complexity, with existing solutions ignoring underspecified phonetic features such as stress. We present a framework for more phonetically accurate Hebrew TTS with four contributions: (1) Phonikud, an open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified International Phonetic Alphabet (IPA) transcriptions, designed by augmenting a base diacritizer. (2) The ILSpeech corpus of paired Hebrew audio, text, and expert IPA annotations. (3) A benchmark for the previously unmeasured task of Hebrew G2P conversion. (4) Hebrew audio-to-IPA models capturing previously disregarded phonetic details for automatic TTS evaluation. Our results show that Phonikud more accurately predicts Hebrew phonemes than prior methods, and that small, local TTS models with phonetic input from Phonikud approach large proprietary systems. We release our code, data, and models at https://phonikud.github.io.