10 citations · 11 across the 13 of their papers we have counts for
16 papers
Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
Xuanru Zhou, Yiwen Shao, Jiahong Li +1
Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimi…
Optimal physical approximations of pure-state cloning and transposition are complementary channels
Vanessa Brzić, Dmitry Grinko, Michał Studziński +1
State cloning and state transposition are fundamental transformations that, despite being desirable, cannot be perfectly realised as a consequence of two distinct requirements of q…
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…
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation
Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo +3
Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored. Crucially, there is no consensus on whether audio-langu…
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…
K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function
Shuhe Li, Chenxu Guo, Jiachen Lian +18
Evaluating young children's language is challenging for automatic speech recognizers due to high-pitched voices, prolonged sounds, and limited data. We introduce K-Function, a fram…
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…
SoK: Dataset Copyright Auditing in Machine Learning Systems
Linkang Du, Xuanru Zhou, Min Chen +5
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data.…
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{…