3 citations · 8 across the 13 of their papers we have counts for
7 papers · 1 filter
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can
Yi-Jen Shih, Puyuan Peng, Haohe Liu +2
Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on this foundation, recent architec…
MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant
Yi-Jen Shih, Shih-Yun Shan Kuan, Guan-Ting Lin +7
Conversational voice agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end architectures. However, while…
RetroThinker: Enabling Retrospective Thinking in Speech LLMs
Yi-Jen Shih, Puyuan Peng, Abdelrahman Mohamed +1
Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-ba…
Unifying Model and Layer Fusion for Speech Foundation Models
Yi-Jen Shih, David Harwath
Speech Foundation Models have gained significant attention recently. Prior works have shown that the fusion of representations from multiple layers of the same model or the fusion…
Self-supervised Speech Models for Word-Level Stuttered Speech Detection
Yi-Jen Shih, Zoi Gkalitsiou, Alexandros G. Dimakis +1
Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and…
Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
Hung-Chieh Fang, Nai-Xuan Ye, Yi-Jen Shih +5
Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks. However, these models predominantly centered on frame-level training o…
AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Yuan Tseng, Layne Berry, Yi-Ting Chen +16
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models of…