# BranchShine-CR: Compact Multilingual IPA Transcription with Self-Conditioned CTC and Consistency Regularization

arXiv: https://arxiv.org/abs/2609.29069 · HTML: https://mlsanity.com/paper/2609.29069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [R-Drop: Regularized Dropout for Neural Networks](https://mlsanity.com/paper/2106.14448/) (2021)
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition](https://mlsanity.com/paper/2210.00077/) (2022)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding](https://mlsanity.com/paper/2207.02971/) (2022)
- [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)
- [Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions](https://mlsanity.com/paper/2104.02724/) (2021)
- [Towards Robust Speech Representation Learning for Thousands of Languages](https://mlsanity.com/paper/2407.00837/) (2024)
- [Moonshine: Speech Recognition for Live Transcription and Voice Commands](https://mlsanity.com/paper/2410.15608/) (2024)
- [ZIPA: A family of efficient models for multilingual phone recognition](https://mlsanity.com/paper/2505.23170/) (2025)
- [CR-CTC: Consistency regularization on CTC for improved speech recognition](https://mlsanity.com/paper/2410.05101/) (2024)
- [Universal Automatic Phonetic Transcription into the International Phonetic Alphabet](https://mlsanity.com/paper/2308.03917/) (2023)
- [POWSM: A Phonetic Open Whisper-Style Speech Foundation Model](https://mlsanity.com/paper/2510.24992/) (2025)
- [Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes](https://mlsanity.com/paper/2306.04306/) (2023)
- [AlloVera: A Multilingual Allophone Database](https://mlsanity.com/paper/2004.08031/) (2020)
- [An Empirical Recipe for Universal Phone Recognition](https://mlsanity.com/paper/2603.29042/) (2026)
- [BranchShine: Compact Raw-Audio-to-IPA Transcription with a RoPE E-Branchformer Encoder](https://mlsanity.com/paper/2606.22824/) (2026)

## Abstract (arXiv)

We introduce BranchShine-CR, a 25M-parameter model for multilingual transcription into the International Phonetic Alphabet (IPA). It combines log-mel features, a rotary-position E-Branchformer encoder, intermediate self-conditioned connectionist temporal classification (CTC), and consistency regularization across augmented views. On 16,646 shared IPApack++ test utterances, it achieves 4.47% IPA character error rate, a 22.3% relative reduction from ZIPA-CTC-NS, with approximately one-twelfth as many parameters while being trained from scratch. BranchShine-CR also outperforms a similarly sized NeMo Conformer baseline across all 41 dataset language labels. Ablation studies indicate the individual components synergetically acting in model performance contribution. These findings support compact IPA recognition capabilities under limited compute budget, for applications in low-resource on-device pronunciation assessment.