Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
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Computer Science > Computation and Language
Title:Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
Abstract:Self-supervised and supervised speech models are increasingly used to investigate which linguistic information their internal representations encode, and at what level of abstraction they encode it. One underexplored phenomenon is consonant cluster reduction (CCR) in African American English (AAE), a widespread phonological process and a source of automatic speech recognition (ASR) disparity. To examine how CCR is represented, we conduct speaker-independent layer-wise probing of wav2vec2-base and Whisper-small using two tasks: segmental reduction detection and segmental restoration of underlying cluster identity. Both models distinguish reduced and canonical forms with high accuracy. Crucially, reduced segments retain cues to their underlying stops, indicating that CCR is encoded as structured gradient phonological variation rather than simple segmental deletion. These results demonstrate structured phonological encoding of AAE CCR patterns in modern speech models.
| Comments: | This paper has been accepted for presentation at Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.23948 [cs.CL] |
| (or arXiv:2606.23948v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23948
arXiv-issued DOI via DataCite (pending registration)
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