4 papers
Scaling Spoken Language Models with Syllabic Speech Tokenization
Nicholas Lee, Cheol Jun Cho, Alan W Black +1
Spoken language models (SLMs) typically discretize speech into high-frame-rate tokens extracted from SSL speech models. As the most successful LMs are based on the Transformer arch…
Sylber 2.0: A Universal Syllable Embedding
Cheol Jun Cho, Nicholas Lee, Alan W Black +1
Scaling spoken language modeling requires speech tokens that are both efficient and universal. Recent work has proposed syllables as promising speech tokens at low temporal resolut…
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT
Cheol Jun Cho, Abdelrahman Mohamed, Shang-Wen Li +2
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing. Yet, the discovered units often remain in phonetic s…
Sylber: Syllabic Embedding Representation of Speech from Raw Audio
Cheol Jun Cho, Nicholas Lee, Akshat Gupta +4
Syllables are compositional units of spoken language that efficiently structure human speech perception and production. However, current neural speech representations lack such str…