53 citations · 67 across the 25 of their papers we have counts for
9 papers · 1 filter
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…
Decoupling Conversational Dynamics in Full-Duplex Spoken Models through Reinforcement Learning
Yuxin Li, Donghang Wu, Guan-Ting Lin +4
Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, an…
Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examiner
Guan-Ting Lin, Shih-Yun Shan Kuan, Jiatong Shi +4
While full-duplex speech agents enable natural, low-latency interaction by speaking and listening simultaneously, their consistency and task performance in multi-turn settings rema…
Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
Kai-Wei Chang, En-Pei Hu, Chun-Yi Kuan +7
Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the abil…
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…
Property Neurons in Self-Supervised Speech Transformers
Tzu-Quan Lin, Guan-Ting Lin, Hung-yi Lee +1
There have been many studies on analyzing self-supervised speech Transformers, in particular, with layer-wise analysis. It is, however, desirable to have an approach that can pinpo…
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech
Guan-Ting Lin, Wei-Ping Huang, Hung-yi Lee
Deep Learning-based end-to-end Automatic Speech Recognition (ASR) has made significant strides but still struggles with performance on out-of-domain samples due to domain shifts in…
Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition
Guan-Ting Lin, Shang-Wen Li, Hung-yi Lee
Although deep learning-based end-to-end Automatic Speech Recognition (ASR) has shown remarkable performance in recent years, it suffers severe performance regression on test sample…
Analyzing the Robustness of Unsupervised Speech Recognition
Guan-Ting Lin, Chan-Jan Hsu, Da-Rong Liu +2
Unsupervised speech recognition (unsupervised ASR) aims to learn the ASR system with non-parallel speech and text corpus only. Wav2vec-U has shown promising results in unsupervised…