10 citations · 11 across the 13 of their papers we have counts for
6 papers · 1 filter
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{…