4 citations · 6 across the 22 of their papers we have counts for
23 papers
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…
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…
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…
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…
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…
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…
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…
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…
[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…
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…
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj +5
Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-pho…
Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation
Karen Rosero, Eunjung Yeo, David R. Mortensen +3
We present ChiReSSD, a speech reconstruction framework that preserves children speaker's identity while suppressing mispronunciations. Unlike prior approaches trained on healthy ad…
CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
Brian Yan, Injy Hamed, Shuichiro Shimizu +24
We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4…
Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages
Chin-Jou Li, Eunjung Yeo, Kwanghee Choi +7
Automatic speech recognition (ASR) for dysarthric speech remains challenging due to data scarcity, particularly in non-English languages. To address this, we fine-tune a voice conv…
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment
Kwanghee Choi, Eunjung Yeo, Kalvin Chang +2
Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment. Modeling allophones is crucial for atypical pronunciation assessment,…
Voice Biomarker Analysis and Automated Severity Classification of Dysarthric Speech in a Multilingual Context
Eunjung Yeo
Dysarthria, a motor speech disorder, severely impacts voice quality, pronunciation, and prosody, leading to diminished speech intelligibility and reduced quality of life. Accurate…
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription
Eun Jung Yeo, Hyungshin Ryu, Jooyoung Lee +2
This paper presents a large-scale analysis of L2 Korean pronunciation error patterns from five different language backgrounds, Chinese, Vietnamese, Japanese, Thai, and English, by…
Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification
Eun Jung Yeo, Kwanghee Choi, Sunhee Kim +1
This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes Uncertainty Quantification (UQ) for automatic speech intelligibility assessment for dysarthric speech.…
Opening the Black Box of wav2vec Feature Encoder
Kwanghee Choi, Eun Jung Yeo
Self-supervised models, namely, wav2vec and its variants, have shown promising results in various downstream tasks in the speech domain. However, their inner workings are poorly un…
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning
Eun Jung Yeo, Kwanghee Choi, Sunhee Kim +1
Automatic assessment of dysarthric speech is essential for sustained treatments and rehabilitation. However, obtaining atypical speech is challenging, often leading to data scarcit…
Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil
Eun Jung Yeo, Kwanghee Choi, Sunhee Kim +1
This paper proposes a cross-lingual classification method for English, Korean, and Tamil, which employs both language-independent features and language-unique features. First, we e…
Multilingual analysis of intelligibility classification using English, Korean, and Tamil dysarthric speech datasets
Eun Jung Yeo, Sunhee Kim, Minhwa Chung
This paper analyzes dysarthric speech datasets from three languages with different prosodic systems: English, Korean, and Tamil. We inspect 39 acoustic measurements which reflect t…