16 citations · 30 across the 11 of their papers we have counts for
11 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…
Fast, High-Quality and Parameter-Efficient Articulatory Synthesis using Differentiable DSP
Yisi Liu, Bohan Yu, Drake Lin +3
Articulatory trajectories like electromagnetic articulography (EMA) provide a low-dimensional representation of the vocal tract filter and have been used as natural, grounded featu…
Coding Speech through Vocal Tract Kinematics
Cheol Jun Cho, Peter Wu, Tejas S. Prabhune +2
Vocal tract articulation is a natural, grounded control space of speech production. The spatiotemporal coordination of articulators combined with the vocal source shapes intelligib…
Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection
Jiachen Lian, Carly Feng, Naasir Farooqi +7
Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primari…
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…
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Cheol Jun Cho, Edward F. Chang, Gopala K. Anumanchipalli
Understanding the neural implementation of complex human behaviors is one of the major goals in neuroscience. To this end, it is crucial to find a true representation of the neural…
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
Peter Wu, Li-Wei Chen, Cheol Jun Cho +4
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interp…
Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech
Cheol Jun Cho, Peter Wu, Abdelrahman Mohamed +1
Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks. To understand such util…