Does A Priori Phonological Knowledge Improve Cross-Lingual Robustness of Phonemic Contrasts?

For speech models that depend on sharing between phonological representations an often overlooked issue is that phonological contrasts that are succinctly described language-internally by the phonemes and their respective featurizations are not necessarily robust across languages. This paper extends a recently proposed method for assessing the cross-linguistic consistency of phonological features in phoneme inventories. The original method employs binary neural classifiers for individual phonological contrasts trained solely on audio. This method cannot resolve some important phonological contrasts, such as retroflex consonants, cross-linguistically. We extend this approach by leveraging prior phonological knowledge during classifier training. We observe that since phonemic descriptions are articulatory rather than acoustic the model input space needs to be grounded in phonology to better capture phonemic correlations between the training samples. The cross-linguistic consistency of the proposed method is evaluated in multilingual setting on held-out low-resource languages and classification quality is reported. We observe modest gains over the baseline for difficult cases, such as cross-lingual detection of aspiration, and discuss multiple confounding factors that explain the dimensions of the difficulty for this task.

• Speech Processing

• Natural Language Processing