Pretrained self-supervised speech models can recognize unseen consonants
Chihiro Taguchi, Éric Le Ferrand, Hirosi Nakagawa, Hitomi Ono, Kanji Kato, Emily Prud'hommeaux, David Chiang
Abstract
Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations. However, their training data are heavily skewed toward high-resource languages with little data from low-resource languages, raising concerns about the potential underrepresentation of typologically uncommon speech sounds such as click consonants primarily found in Khoisan languages. This leads to our central research question: Can these models recognize click consonants as accurately as other speech sounds? To address this question, we fine-tune and compare pretrained self-supervised speech models (Wav2Vec2 and HuBERT) on data from two click-rich Khoisan languages (G|ui and West !Xoon). Our results reveal that the fine-tuned models consistently recognize clicks more accurately than non-clicks, suggesting that self-supervision enables generalization across human speech sounds including rare phonemes.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.