8 citations · 15 across the 33 of their papers we have counts for
23 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…
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…
Speech World Model: Causal State-Action Planning with Explicit Reasoning for Speech
Xuanru Zhou, Jiachen Lian, Henry Hong +2
Current speech-language models (SLMs) typically use a cascade of speech encoder and large language model, treating speech understanding as a single black box. They analyze the cont…
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…
Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Models
Guan-Ting Lin, Shih-Yun Shan Kuan, Qirui Wang +4
Full-duplex spoken dialogue systems promise to transform human-machine interaction from a rigid, turn-based protocol into a fluid, natural conversation. However, the central challe…
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection
Jinming Zhang, Xuanru Zhou, Jiachen Lian +10
Speech dysfluency detection is crucial for clinical diagnosis and language assessment, but existing methods are limited by the scarcity of high-quality annotated data. Although rec…
Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis
Zongli Ye, Jiachen Lian, Xuanru Zhou +14
Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model pho…
Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection
Chenxu Guo, Jiachen Lian, Xuanru Zhou +13
Automatic detection of speech dysfluency aids speech-language pathologists in efficient transcription of disordered speech, enhancing diagnostics and treatment planning. Traditiona…
SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies
Jiachen Lian, Xuanru Zhou, Zoe Ezzes +6
Speech is a hierarchical collection of text, prosody, emotions, dysfluencies, etc. Automatic transcription of speech that goes beyond text (words) is an underexplored problem. We f…
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…
YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection
Xuanru Zhou, Anshul Kashyap, Steve Li +9
Dysfluent speech detection is the bottleneck for disordered speech analysis and spoken language learning. Current state-of-the-art models are governed by rule-based systems which l…
SSDM: Scalable Speech Dysfluency Modeling
Jiachen Lian, Xuanru Zhou, Zoe Ezzes +6
Speech dysfluency modeling is the core module for spoken language learning, and speech therapy. However, there are three challenges. First, current state-of-the-art solutions\cite{…
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…
Deep Speech Synthesis from MRI-Based Articulatory Representations
Peter Wu, Tingle Li, Yijing Lu +6
In this paper, we study articulatory synthesis, a speech synthesis method using human vocal tract information that offers a way to develop efficient, generalizable and interpretabl…
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations
Jiachen Lian, Alexei Baevski, Wei-Ning Hsu +1
Self-supervision has shown great potential for audio-visual speech recognition by vastly reducing the amount of labeled data required to build good systems. However, existing metho…
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization
Jiachen Lian, Alan W Black, Yijing Lu +3
Articulatory representation learning is the fundamental research in modeling neural speech production system. Our previous work has established a deep paradigm to decompose the art…
Towards Improved Zero-shot Voice Conversion with Conditional DSVAE
Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli +1
Disentangling content and speaking style information is essential for zero-shot non-parallel voice conversion (VC). Our previous study investigated a novel framework with disentang…
Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE
Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli +1
In this paper, we propose a novel unsupervised text-to-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. UTTS is a multi-speaker speech sy…
Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition
Jiachen Lian, Alan W Black, Louis Goldstein +1
Most of the research on data-driven speech representation learning has focused on raw audios in an end-to-end manner, paying little attention to their internal phonological or gest…
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
Jiachen Lian, Chunlei Zhang, Dong Yu
Traditional studies on voice conversion (VC) have made progress with parallel training data and known speakers. Good voice conversion quality is obtained by exploring better alignm…