9 papers · 1 filter
HASS: Hierarchical Simulation of Logopenic Aphasic Speech for Scalable PPA Detection
Harrison Li, Kevin Wang, Cheol Jun Cho +13
Building a diagnosis model for primary progressive aphasia (PPA) has been challenging due to the data scarcity. Collecting clinical data at scale is limited by the high vulnerabili…
Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces
Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho +2
Transformer-based self-supervised speech models (S3Ms) are often described as contextualized, yet what this entails remains unclear. Here, we focus on how a single frame-level S3M…
HuPER: A Human-Inspired Framework for Phonetic Perception
Chenxu Guo, Jiachen Lian, Yisi Liu +9
We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and linguistic knowledge. With only 100 hours of…
[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic
Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho +2
Self-supervised speech models (S3Ms) are known to encode rich phonetic information, yet how this information is structured remains underexplored. We conduct a comprehensive study a…
Teaching Machines to Speak Using Articulatory Control
Akshay Anand, Chenxu Guo, Cheol Jun Cho +2
Current speech production systems predominantly rely on large transformer models that operate as black boxes, providing little interpretability or grounding in the physical mechani…
LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
Zongli Ye, Jiachen Lian, Akshaj Gupta +18
Phonetic speech transcription is crucial for fine-grained linguistic analysis and downstream speech applications. While Connectionist Temporal Classification (CTC) is a widely used…
Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Xuanru Zhou, Jiachen Lian, Cheol Jun Cho +12
Phonetic error detection, a core subtask of automatic pronunciation assessment, identifies pronunciation deviations at the phoneme level. Speech variability from accents and dysflu…
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection
Xuanru Zhou, Jiachen Lian, Cheol Jun Cho +10
Speech dysfluency modeling is a task to detect dysfluencies in speech, such as repetition, block, insertion, replacement, and deletion. Most recent advancements treat this problem…
Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection
Xuanru Zhou, Cheol Jun Cho, Ayati Sharma +9
Current de-facto dysfluency modeling methods utilize template matching algorithms which are not generalizable to out-of-domain real-world dysfluencies across languages, and are not…