5 citations · 7 across the 9 of their papers we have counts for
12 papers
Repairing Speech Access After Text Updates with Frozen Affine Fields
Ming-Hao Hsu, Yuxiang Wang, Yingda Shen +1
Speech-capable language models inherit most of their knowledge from text-rich language backbones, and continued model maintenance is likewise dominated by text updates. Knowledge a…
DuplexAgent-RSI: Recursive Harness Improvement for Full-Duplex Voice Agent Collaboration
Yingda Shen, Yuxiang Wang, Kunyu Feng +7
Voice agents are converging on a collaboration pattern: a full-duplex interaction model stays on the live channel as the entry to the conversation, while search, reasoning, and cod…
AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models
Yuxiang Wang, Kunyu Feng, Yuancheng Wang +12
Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT)…
EvoAudio: Recursive Self-Improvement for Audio Understanding
Yuxiang Wang, Shengbo Cai, Yingda Shen +6
Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is costly, labels from stronger mod…
Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention
Ming-Hao Hsu, Xiaohai Tian, Jun Zhang +1
Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this gap is not a uniform cognitive deficit. Evaluating two architect…
Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs
Ming-Hao Hsu, Yuxuan Hu, Shujie Liu +3
Large language models (LLMs) provide a powerful reasoning backbone for speech understanding, but integrating continuous acoustic signals into a pretrained LLM remains challenging.…
Geometric Alignment without Functional Equivalence: A Layer-wise Analysis of the Speech-Text Modality Gap
Ming-Hao Hsu, Xueyao Zhang, Xiaohai Tian +4
End-to-end speech large language models (LLMs) often answer the same question less accurately from speech than from text, even when the spoken content matches the text prompt. Exis…
Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration
Ryan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang +8
Multilingual speech foundation models such as Whisper are trained on web-scale data, where data for each language consists of a myriad of regional varieties. However, different reg…
TASLA: Text-Aligned Speech Tokens with Multiple Layer-Aggregation
Ming-Hao Hsu, Liang-Hsuan Tseng, Hung-yi Lee +1
We propose Text-Aligned Speech Tokens with Multiple Layer-Aggregation (TASLA), which is a text-aligned speech tokenization framework that aims to address the problem that under a l…
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition
Ming-Hao Hsu, Hung-yi Lee
Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource languages but face significant challenges when applied to low-resource languages due…
GSQA: An End-to-End Model for Generative Spoken Question Answering
Min-Han Shih, Ho-Lam Chung, Yu-Chi Pai +4
In recent advancements in spoken question answering (QA), end-to-end models have made significant strides. However, previous research has primarily focused on extractive span selec…
Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks
Ming-Hao Hsu, Kai-Wei Chang, Shang-Wen Li +1
Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an essential role in utilizing large language models (LLMs)…