Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization

Ryota Komatsu, Kota Kawakita, Takuma Okamoto, Takahiro Shinozaki

Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization: 1 upvotes on Hugging Face Daily Papers, #39 of 40 papers on 2026-07-07. Day-by-day upvote history.

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 chunks. Experimental results demonstrate that our proposed method achieves state-of-the-art performance in syllable boundary detection and syllabic segment clustering. Moreover, a speech language model trained on our syllabic tokens achieves a 7% relative improvement in syntactic and semantic understanding over the phone-level SpiRit-LM.

Paper page on Hugging Face · arXiv

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