1 citations · 1 across the 6 of their papers we have counts for
6 papers
Voice Memory for Agentic Speech Recognition
Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko +3
We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.md and decides per utterance wh…
SpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models
Liang-Yuan Wu, Zih-Ching Chen, Tongshuang Wu +2
As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural hu…
Beyond Human-prompting: Adaptive Prompt Tuning with Semantic Alignment for Anomaly Detection
Pi-Wei Chen, Jerry Chun-Wei Lin, Wei-Han Chen +4
Pre-trained Vision-Language Models (VLMs) have recently shown promise in detecting anomalies. However, previous approaches are fundamentally limited by their reliance on human-desi…
Continual Pre-Training is (not) What You Need in Domain Adaption
Pin-Er Chen, Da-Chen Lian, Shu-Kai Hsieh +8
The recent advances in Legal Large Language Models (LLMs) have transformed the landscape of legal research and practice by automating tasks, enhancing research precision, and suppo…
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko +11
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The an…
Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme
Pi-Wei Chen, Jerry Chun-Wei Lin, Jia Ji +3
Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning, making prompt-based anomaly detection a promising approach. Tr…