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.

Abstract Curating datasets that span multiple languages is challenging. To make the collection more scalable, researchers often incorporate one or more imperfect classifiers in the process, like language identification models. These models, however, are prone to failure, resulting in some language partitions being unreliable for downstream tasks. We introduce a statistical test, the Preference Proportion Test, for identifying such unreliable partitions. By annotating only 20 samples for a language partition, we are able to identify systematic transcription errors for 10 language partitions in a recent large multilingual transcribed audio archive, X-IPAPack (Zhu et al., 2024). We find that filtering these low-quality partitions out when training models for the downstream task of phonetic transcription brings substantial benefits, most notably a 25.7% relative improvement on transcribing recordings in out-of-distribution languages. Our work contributes an effective method for auditing multilingual audio archives.1

To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses.These studies often rely on coarse corpuslevel statistics or local word-level analyses, mainly in English.We introduce the INFOGAP method-an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages.We evaluate INFOGAP by analyzing LGBT people's portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias.We find large discrepancies in factual coverage across the languages.Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia.Crucially, INFOGAP both facilitates large scale analyses, and pinpoints local document-and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale. 1

Jian Zhu, Changbing Yang, Farhan Samir, Jahurul Islam. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Data augmentation techniques are widely used in low-resource automatic morphological inflection to overcome data sparsity.However, the full implications of these techniques remain poorly understood.In this study, we aim to shed light on the theoretical aspects of the prominent data augmentation strategy STEM-CORRUPT (Silfverberg et al., 2017;Anastasopoulos and Neubig, 2019), a method that generates synthetic examples by randomly substituting stem characters in gold standard training examples.To begin, we conduct an information-theoretic analysis, arguing that STEMCORRUPT improves compositional generalization by eliminating spurious correlations between morphemes, specifically between the stem and the affixes.Our theoretical analysis further leads us to study the sampleefficiency with which STEMCORRUPT reduces these spurious correlations.Through evaluation across seven typologically distinct languages, we demonstrate that selecting a subset of datapoints with both high diversity and high predictive uncertainty significantly enhances the data-efficiency of STEMCORRUPT.However, we also explore the impact of typological features on the choice of the data selection strategy and find that languages incorporating a high degree of allomorphy and phonological alternations derive less benefit from synthetic examples with high uncertainty.We attribute this effect to phonotactic violations induced by STEMCORRUPT, emphasizing the need for further research to ensure optimal performance across the entire spectrum of natural language morphology. 1

This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.