1 citations · 2 across the 13 of their papers we have counts for
8 papers · 1 filter
SUTA-LM: Bridging Test-Time Adaptation and Language Model Rescoring for Robust ASR
Wei-Ping Huang, Guan-Ting Lin, Hung-yi Lee
Despite progress in end-to-end ASR, real-world domain mismatches still cause performance drops, which Test-Time Adaptation (TTA) aims to mitigate by adjusting models during inferen…
Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities
Guan-Ting Lin, Jiachen Lian, Tingle Li +4
Spoken dialogue modeling poses challenges beyond text-based language modeling, requiring real-time interaction, turn-taking, and backchanneling. While most Spoken Dialogue Models (…
Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback
Guan-Ting Lin, Prashanth Gurunath Shivakumar, Aditya Gourav +4
While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of…
Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?
Guan-Ting Lin, Hung-yi Lee
Emphasis is a crucial component in human communication, which indicates the speaker's intention and implication beyond pure text in dialogue. While Large Language Models (LLMs) hav…
Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations
Guan-Ting Lin, Cheng-Han Chiang, Hung-yi Lee
In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing para…
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe +6
Large Language Models (LLMs) have demonstrated superior abilities in tasks such as chatting, reasoning, and question-answering. However, standard LLMs may ignore crucial paralingui…
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
Kevin Everson, Yile Gu, Huck Yang +10
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with tr…
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering
Chyi-Jiunn Lin, Guan-Ting Lin, Yung-Sung Chuang +5
Spoken Question Answering (SQA) is essential for machines to reply to user's question by finding the answer span within a given spoken passage. SQA has been previously achieved wit…