Pretrained self-supervised speech models can recognize unseen consonants
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 f...
| Authors | Chihiro Taguchi, Éric Le Ferrand, Hirosi Nakagawa et al. |
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| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 8 October 2026 |
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