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

arXiv:2609.29069 · cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Consistency — 17× in this paper · explained in 3 episodes
• Regularization — 8× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Generative AI — 2× in this paper · explained in 2 episodes
• International Phonetic Alphabet — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Activation Checkpointing — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• An Empirical Recipe for Universal Phone Recognition
• BranchShine: Compact Raw-Audio-to-IPA Transcription with a RoPE E-Branchformer Encoder
• POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
• E-Branchformer: Branchformer with Enhanced merging for speech recognition
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• Moonshine: Speech Recognition for Live Transcription and Voice Commands
• CR-CTC: Consistency regularization on CTC for improved speech recognition

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks