Perceptual compensation for tonal context in self-supervised speech models
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Computer Science > Computation and Language
Title:Perceptual compensation for tonal context in self-supervised speech models
Abstract:This study examines the extent to which the wav2vec2.0 architecture exhibits evidence of compensation for phonological context. We conducted a pseudo-replication of a perceptional compensation experiment on Mandarin Chinese tones, and compared the embedding similarities and probing classifier outputs between a purely self-supervised pre-trained model and a model fine-tuned for Mandarin ASR. No evidence of compensation was found in the embedding similarities of the purely pre-trained model. Probing classifiers showed some evidence of compensation in addition to the expected layer-wise improvements in categorization, but failed to replicate human performance on isolated test syllables. Our findings contrast with previous reports of sensitivity to phonological structure emerging through pre-training alone, and suggest that supervised objectives may be necessary to encourage the abstraction of at least some types of phonological regularities.
| Comments: | Accepted for publication at Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.17835 [cs.CL] |
| (or arXiv:2606.17835v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17835
arXiv-issued DOI via DataCite (pending registration)
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