Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization
Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech. Recent syllabic tokenization methods employ teacher-student distillation of the pretrained HuBERT to organize latent speech frame representations into syllabic segments. However, when trained with an utterance-level cross-entropy objective, the model predicts speaker identity rather than linguistic content, thereby compromising the purity of syllabic tokens. To address this problem, we propose a speaker-disentangled syllabic tokenizer that regresses speaker-perturbed student representations toward clean teacher targets within fixed-length chun...
| Authors | Ryota Komatsu, Kota Kawakita, Takuma Okamoto et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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