OLaPh (Optimal Language Phonemizer) is a multilingual phonemization framework that converts text into phonemes surpassing the quality of comparable frameworks.

Traditional phonemizers rely on simple rule-based mappings or lexicon lookups. Neural and hybrid approaches improve generalization but still struggle with:

• Names and foreign words
• Abbreviations and acronyms
• Loanwords and compounds
• Ambiguous homographs

OLaPh tackles these challenges by combining:

• Extensive language-specific dictionaries
• Abbreviation, number, and letter normalization
• Compound resolution with probabilistic scoring
• Cross-language handling
• NLP-based preprocessing via spaCy and Lingua

Evaluations in German and English show improved accuracy and robustness over existing phonemizers, including on challenging multilingual datasets.

• Multilingual phonemization (DE, EN, FR, ES)
• Abbreviation and letter pronunciation dictionaries
• Number normalization
• Cross-language acronym detection
• Compound splitting with probabilistic scoring
• Freely available lexica for research and development derived from wiktionary.org.

A LLM based on OLaPh output is also available. It is a GemmaX 2B Model trained on ~10M sentences derived from the FineWeb Corpus phonemized with the OLaPh framework.

Find it here on huggingface

pip install olaph
python -m spacy download de_core_news_sm
python -m spacy download en_core_web_sm
python -m spacy download es_core_news_sm
python -m spacy download fr_core_news_sm

git clone https://github.com/iisys-hof/olaph.git
cd olaph
pip install -e .
python -m spacy download de_core_news_sm
python -m spacy download en_core_web_sm
python -m spacy download es_core_news_sm
python -m spacy download fr_core_news_sm

from olaph import Olaph

phonemizer = Olaph()

output = phonemizer.phonemize_text("He ordered a Brezel and a beer in a tavern near München.", lang="en")

print(output)

• spaCy
• Lingua
• num2words
• inflect

Phonemization, the conversion of text into phonemes, is a key step in text-to-speech. Traditional approaches use rule-based transformations and lexicon lookups, while more advanced methods apply preprocessing techniques or neural networks for improved accuracy on out-of-domain vocabulary. However, all systems struggle with names, loanwords, abbreviations, and homographs. This work presents OLaPh (Optimal Language Phonemizer), a framework that combines large lexica, multiple NLP techniques, and compound resolution with a probabilistic scoring function. Evaluations in German and English show improved accuracy over previous approaches, including on a challenging dataset. To further address unresolved cases, we train a large language model on OLaPh-generated data, which achieves even stronger generalization and performance. Together, the framework and LLM improve phonemization consistency and provide a freely available resource for future research.

If you use OLaPh in academic work, please cite:

@misc{wirth2025olaphoptimallanguagephonemizer,
      title={OLaPh: Optimal Language Phonemizer},
      author={Johannes Wirth},
      year={2025},
      eprint={2509.20086},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.20086},
}