Probing in the Wild: A Case Study of Self-Supervised Speech Representations on Mandarin Sub-dialects with Unsupervised Articulatory Analysis
arXiv:2606.25459v1 Announce Type: new Abstract: While self-supervised speech models have achieved strong performance across speech tasks, relatively little is known about how their internal phonetic representations behave under fine-grained dialect variation. Existing probing studies typically rely on curated corpora with manual phonetic annotations, limiting their applicability to naturally occurring dialect speech. We present a case study of articulatory feature representations in a Mandarin self-supervised speech model using an entirely unlabeled probing pipeline. Phone sequences are genera
The proliferation of self-supervised speech models necessitates deeper understanding of their internal representations, especially for diverse linguistic data like dialects, an area of active research.
Understanding how AI models process and differentiate fine-grained phonetic variations is crucial for developing robust, fair, and globally applicable speech technologies, impacting fundamental AI capabilities.
This research provides a new methodology for evaluating self-supervised speech models on 'in the wild' dialectal data without manual labels, enabling broader and more efficient analysis.
- · AI researchers
- · Speech technology developers
- · Companies seeking to deploy AI in diverse linguistic contexts
- · Developers of speech AI with limited dialectal robustness
Improved understanding and interpretability of self-supervised speech model representations for less common language variants.
Development of more accurate and inclusive speech AI systems capable of handling significant linguistic diversity.
Accelerated deployment of speech AI solutions in complex multilingual and dialectal environments, potentially impacting broader AI adoption and accessibility.
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