# Universal Automatic Phonetic Transcription into the International Phonetic Alphabet

arXiv: https://arxiv.org/abs/2308.03917 · HTML: https://mlsanity.com/paper/2308.03917/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Sequence-based Multi-lingual Low Resource Speech Recognition](https://mlsanity.com/paper/1802.07420/) (2018)

## Built on by (most cited first, 16 of 16)

- [ZIPA: A family of efficient models for multilingual phone recognition](https://mlsanity.com/paper/2505.23170/) (2025)
- [POWSM: A Phonetic Open Whisper-Style Speech Foundation Model](https://mlsanity.com/paper/2510.24992/) (2025)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis](https://mlsanity.com/paper/2501.06810/) (2025)
- [An Empirical Recipe for Universal Phone Recognition](https://mlsanity.com/paper/2603.29042/) (2026)
- [Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens](https://mlsanity.com/paper/2403.17407/) (2024)
- [Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages](https://mlsanity.com/paper/2604.18204/) (2026)
- [Efficiently Identifying Low-Quality Language Subsets in Multilingual Datasets: A Case Study on a Large-Scale Multilingual Audio Dataset](https://mlsanity.com/paper/2410.04292/) (2024)
- [Building Tailored Speech Recognizers for Japanese Speaking Assessment](https://mlsanity.com/paper/2509.20655/) (2025)
- [Scaling Human and G2P Supervision for Robust Phonetic Transcription](https://mlsanity.com/paper/2606.16019/) (2026)
- [BranchShine: Compact Raw-Audio-to-IPA Transcription with a RoPE E-Branchformer Encoder](https://mlsanity.com/paper/2606.22824/) (2026)
- [Selective Augmentation: Improving Universal Automatic Phonetic Transcription via G2P Bootstrapping](https://mlsanity.com/paper/2604.27204/) (2026)
- [ArtNet: A JEPA-Like Articulatory Predictive Framework for Robust Zero-Shot Phoneme Recognition](https://mlsanity.com/paper/2606.16595/) (2026)
- [BranchShine-CR: Compact Multilingual IPA Transcription with Self-Conditioned CTC and Consistency Regularization](https://mlsanity.com/paper/2609.29069/) (2026)
- [Deep Learning Techniques for Phoneme Recognition in Italian Children' s Speech](https://mlsanity.com/paper/2609.33060/) (2026)
- [SHAMS: An Audio-Grounded Pronunciation Benchmark for Levantine Arabic](https://mlsanity.com/paper/2610.01427/) (2026)

## Abstract (arXiv)

This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA). Transcription of spoken languages into IPA is an essential yet time-consuming process in language documentation, and even partially automating this process has the potential to drastically speed up the documentation of endangered languages. Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use training data from seven languages from CommonVoice 11.0, transcribed into IPA semi-automatically. Although this training dataset is much smaller than Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or better results. Furthermore, we show that the quality of our universal speech-to-IPA models is close to that of human annotators.