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20222026
most citedOpening the Black Box of wav2vec Feature Encoder

4 citations · 6 across the 22 of their papers we have counts for

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10 papers · 1 filter

cs.CL2026

Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking

Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia +4

Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritiz…

eess.AS2026

Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection

Minu Kim, Eunjung Yeo, Kwanghee Choi +1

Self-supervised speech models (S3Ms) provide powerful representations for Parkinson's disease (PD) detection, making cross-lingual transfer attractive for languages lacking labeled…

eess.AS2026

Phone Segmentation and Recognition through Phonological Activation Mapping

Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh +8

Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the re…

cs.CL2026

Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations

Haitong Sun, Stephen McIntosh, Kwanghee Choi +3

Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly…

eess.AS2026

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…

cs.CL2026

An Empirical Recipe for Universal Phone Recognition

Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi +4

Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive. Highly performant English-focused models d…

cs.CL2026

Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease

Abner Hernandez, Eunjung Yeo, Kwanghee Choi +12

The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode…

cs.CL2026

Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling

Eunjung Yeo, Julie M. Liss, Visar Berisha +1

The growing prevalence of neurological disorders associated with dysarthria motivates the need for automated intelligibility assessment methods that are applicalbe across languages…

eess.AS2026

[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…

cs.CL2026

PRiSM: Benchmarking Phone Realization in Speech Models

Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim +13

Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in develop…