Multilingual Phonological Feature Recognition with Self-Supervised Speech Models
For speech processing researchers, this work provides a high-performing, language-general phonological feature recognizer that improves cross-lingual generalization.
PhonoQ-2.0, a multilingual frame-level phonological feature recognizer built on self-supervised speech models, achieves 91.3% macro-F1 in-domain and 88.9% out-of-domain, outperforming a CTC phoneme baseline by +8.8 and +8.6 F1 respectively, and improving unseen-language macro-F1 from 66.9% to 73.6%.
Phonological features provide a language-general and linguistically grounded representation of speech. We present PhonoQ-2.0, a multilingual frame-level phonological feature recognizer built on self-supervised speech models. The system directly predicts a structured 22-dimensional feature vector per frame encoding manner, vowel quality, place, and voicing, instead of deriving features from phoneme outputs. To ensure phonologically coherent predictions, we introduce a manner-conditioned gating mechanism that activates valid feature groups. Evaluated across multiple languages and corpora, PhonoQ-2.0 achieves an average macro-F1 of 91.3% in-domain and 88.9% out-of-domain. Compared to a strong CTC phoneme baseline, it delivers consistent gains of +8.8 F1 in-domain and +8.6 out-of-domain on average. In unseen-language evaluation, PhonoQ-2.0 improves macro-F1 from 66.9% to 73.6% (+6.7 on average), with gains of up to +10.8 points.