5 papers
SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement
Yuan Ge, Junxiang Zhang, Xiaoqian Liu +10
Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling end-to-end spoken dialogue systems. However, evaluating these m…
MRGSEM-Sum: An Unsupervised Multi-document Summarization Framework based on Multi-Relational Graphs and Structural Entropy Minimization
Yongbing Zhang, Fang Nan, Shengxiang Gao +3
The core challenge faced by multi-document summarization is the complexity of relationships among documents and the presence of information redundancy. Graph clustering is an effec…
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation
Yuhao Zhang, Xiangnan Ma, Kaiqi Kou +7
The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic fe…
SECodec: Structural Entropy-based Compressive Speech Representation Codec for Speech Language Models
Linqin Wang, Yaping Liu, Zhengtao Yu +5
With the rapid advancement of large language models (LLMs), discrete speech representations have become crucial for integrating speech into LLMs. Existing methods for speech repres…
A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Model
Shengxiang Gao, Fang nan, Yongbing Zhang +3
Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD).…