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arXiv · 2505.23170

ZIPA: A family of efficient models for multilingual phone recognition

Jian Zhu·Farhan Samir·Eleanor Chodroff·David R. Mortensen
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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 IPAPack++, a large-scale multilingual speech corpus with 17,132 hours of normalized phone transcriptions and a novel evaluation set capturing unseen languages and sociophonetic variation. With the large-scale training data, ZIPA, including transducer (ZIPA-T) and CTC-based (ZIPA-CR) variants, leverage the efficient Zipformer backbones and outperform 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.

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Jian Zhu, Farhan Samir, Eleanor Chodroff, David R. Mortensen. 2025-05-29. ZIPA: A family of efficient models for multilingual phone recognition. https://arxiv.org/abs/2505.23170

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