6 citations · 8 across the 8 of their papers we have counts for
12 papers
Understanding the Modality Gap: An Empirical Study on the Speech-Text Alignment Mechanism of Large Speech Language Models
Bajian Xiang, Shuaijiang Zhao, Tingwei Guo +1
End-to-end Large Speech Language Models (LSLMs) have demonstrated impressive conversational generation abilities, yet consistently fall short of traditional pipeline systems on sem…
Comprehend and Talk: Text to Speech Synthesis via Dual Language Modeling
Junjie Cao, Yichen Han, Ruonan Zhang +5
Existing Large Language Model (LLM) based autoregressive (AR) text-to-speech (TTS) systems, while achieving state-of-the-art quality, still face critical challenges. The foundation…
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning
Cheng Wen, Tingwei Guo, Shuaijiang Zhao +2
Recent work shows that reinforcement learning(RL) can markedly sharpen the reasoning ability of large language models (LLMs) by prompting them to "think before answering." Yet whet…
Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data
Shuaijiang Zhao, Tingwei Guo, Bajian Xiang +4
The GPT-4o represents a significant milestone in enabling real-time interaction with large language models (LLMs) through speech, its remarkable low latency and high fluency not on…
ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation
Cheng Wen, Xianghui Sun, Shuaijiang Zhao +3
This paper presents the development and evaluation of ChatHome, a domain-specific language model (DSLM) designed for the intricate field of home renovation. Considering the proven…
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding
Yingting Li, Ambuj Mehrish, Shuai Zhao +5
Fine-tuning is widely used as the default algorithm for transfer learning from pre-trained models. Parameter inefficiency can however arise when, during transfer learning, all the…