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    <abstract>We present ZIPA, a family of efficient speech models that advances the state-of-the-art performance of crosslinguistic phone recognition. We first curated IPA PACK++, a large-scale multilingual speech corpus with 17,000+ hours of normalized phone transcriptions and a novel evaluation set capturing unseen languages and sociophonetic variation. ZIPA, including transducer (ZIPA-T) and CTC-based (ZIPA-CR) variants, leverages the efficient Zipformer backbones and outperforms existing phone recognition systems with much fewer parameters. Further scaling via noisy student training on 11,000+ hours of pseudo-labeled multilingual data yields further improvement. While ZIPA achieves strong performance on benchmarks, error analysis reveals persistent limitations in modeling sociophonetic diversity, underscoring challenges for future research.</abstract>
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%0 Conference Proceedings
%T ZIPA: A family of efficient models for multilingual phone recognition
%A Zhu, Jian
%A Samir, Farhan
%A Chodroff, Eleanor
%A Mortensen, David R.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhu-etal-2025-zipa
%X We present ZIPA, a family of efficient speech models that advances the state-of-the-art performance of crosslinguistic phone recognition. We first curated IPA PACK++, a large-scale multilingual speech corpus with 17,000+ hours of normalized phone transcriptions and a novel evaluation set capturing unseen languages and sociophonetic variation. ZIPA, including transducer (ZIPA-T) and CTC-based (ZIPA-CR) variants, leverages the efficient Zipformer backbones and outperforms existing phone recognition systems with much fewer parameters. Further scaling via noisy student training on 11,000+ hours of pseudo-labeled multilingual data yields further improvement. While ZIPA achieves strong performance on benchmarks, error analysis reveals persistent limitations in modeling sociophonetic diversity, underscoring challenges for future research.
%R 10.18653/v1/2025.acl-long.961
%U https://aclanthology.org/2025.acl-long.961/
%U https://doi.org/10.18653/v1/2025.acl-long.961
%P 19568-19585

Markdown (Informal)

[ZIPA: A family of efficient models for multilingual phone recognition](https://aclanthology.org/2025.acl-long.961/) (Zhu et al., ACL 2025)

• ZIPA: A family of efficient models for multilingual phone recognition (Zhu et al., ACL 2025)

ACL

• Jian Zhu, Farhan Samir, Eleanor Chodroff, and David R. Mortensen. 2025. ZIPA: A family of efficient models for multilingual phone recognition. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19568–19585, Vienna, Austria. Association for Computational Linguistics.