基于自监督语音模型的多语言音韵特征识别
Multilingual Phonological Feature Recognition with Self-Supervised Speech Models
AI总结：
提出PhonoQ-2.0，一种基于自监督语音模型的多语言帧级音韵特征识别器，通过方式条件门控机制直接预测结构化特征向量，在域内和域外均优于CTC基线。
摘要：
音韵特征提供了语言通用且基于语言学的语音表示。我们提出PhonoQ-2.0，一种基于自监督语音模型构建的多语言帧级音韵特征识别器。该系统直接预测每帧的结构化22维特征向量，编码方式、元音质量、发音部位和清浊，而不是从音素输出中推导特征。为确保音韵上一致的预测，我们引入了一种方式条件门控机制，激活有效的特征组。在多种语言和语料库上评估，PhonoQ-2.0在域内平均宏F1为91.3%，域外为88.9%。与强CTC音素基线相比，它在域内平均获得+8.8 F1的持续提升，域外平均+8.6。在未见语言评估中，PhonoQ-2.0将宏F1从66.9%提高到73.6%（平均+6.7），最高提升达+10.8个百分点。
英文摘要：
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.
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