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arXiv Machine Learning By Nikhil Navas, Sergio Chevtchenko, Talisson Damiao, Saeed Afshar

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

Read the original on arXiv Machine Learning →

BranchShine-CR is a 25‑million‑parameter model that transcribes multilingual speech into the International Phonetic Alphabet (IPA). It uses log‑mel features, a rotary‑position E‑Branchformer encoder, intermediate self‑conditioned CTC, and consistency regularization across augmented views. On 16,646 shared IPApack++ test utterances it achieves a 4.47 % IPA character error rate, a 22.3 % relative improvement over ZIPA‑CTC‑NS and outperforms a similarly sized NeMo Conformer baseline across all 41 language labels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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