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

2026-09-24

AI Summary

This study addresses the challenge of deploying large-scale multilingual IPA transcription models on resource-constrained devices by proposing a lightweight speech recognition architecture. The method constructs a compact backbone network using an E-Branchformer encoder integrated with rotary position encoding, and introduces self-conditioned CTC alongside data augmentation consistency regularization for synergistic training optimization. Experimental results demonstrate that the proposed model substantially reduces parameter count while significantly improving recognition accuracy, achieving an IPA character error rate of 4.47%—a 22.3% relative reduction over the baseline. Furthermore, it outperforms Conformer models of comparable scale, effectively facilitating efficient on-device deployment.

Abstract

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.

Problem

• Multilingual IPA Transcription
• Compact Model
• Pronunciation Assessment
• Low-resource
• On-device

Innovation

• Multilingual IPA Transcription
• Self-Conditioned CTC
• Consistency Regularization
• E-Branchformer
• Compact Model