1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
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: 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…
Towards EMG-to-Speech with a Necklace Form Factor
Peter Wu, Ryan Kaveh, Raghav Nautiyal +8
Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We exp…
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…
Self-Supervised Models of Speech Infer Universal Articulatory Kinematics
Cheol Jun Cho, Abdelrahman Mohamed, Alan W Black +1
Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks. These state-of-the-art models have remained blackboxes, but…