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Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from self-supervised speech representations frequently conf...
arXiv:2609.28060v1 Announce Type: new Abstract: Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for...
Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for second-language (L2) pronunciation assessment in...
arXiv:2606. 20137v1 Announce Type: cross Abstract: Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors.
The paper proposes a method called language orthogonalization to improve zero‑shot cross‑lingual audio deepfake detection. By removing language‑dependent variation from self‑supervised speech models using a target‑free ridge map on language‑identification embeddings, the approach consistently lowers equal error rates across six languages and six model backbones. The gains are larger when the target language is more distant in the language‑identification space.
The paper introduces a native-reference phone‑class geometry that measures second‑language pronunciation deviation without needing pronunciation labels, read‑aloud prompts, or matched native recordings. By averaging self‑supervised representations for each phone‑class in a native speech corpus and applying singular value decomposition, the authors create a compact coordinate system. Projecting L2 utterances into this space, they find that distances to native coordinates correlate negatively with holistic speaking proficiency and pronunciation quality, indicating the geometry captures relevant acoustic‑phonetic information for spontaneous L2 speech.