3 papers
cs.CL2026
Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization
Ryota Komatsu, Kota Kawakita, Takuma Okamoto +1
Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech. Recent syllabic tokenization met…
cs.CL2024
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly
Tingyuan Zhu, Shudong Liu, Yidong Wang +4
Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a speci…
cs.CL2024
Self-Supervised Syllable Discovery Based on Speaker-Disentangled HuBERT
Ryota Komatsu, Takahiro Shinozaki
Self-supervised speech representation learning has become essential for extracting meaningful features from untranscribed audio. Recent advances highlight the potential of deriving…