most citedSALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters

Shanbo Cheng, Yu Bao, Qian Cao +23

Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated transl…

cs.LG2025

DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization

Shuaijie She, Yu Bao, Yu Lu +7

We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Rein…

cs.CL2025

Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice

Shanbo Cheng, Yu Bao, Zhichao Huang +25

Simultaneous Interpretation (SI) represents one of the most daunting frontiers in the translation industry, with product-level automatic systems long plagued by intractable challen…

cs.CL2025

SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversation

Wenyi Yu, Siyin Wang, Xiaoyu Yang +7

In order to enable fluid and natural human-machine speech interaction, existing full-duplex conversational systems often adopt modular architectures with auxiliary components such…

eess.AS20251 cited

Solla: Towards a Speech-Oriented LLM That Hears Acoustic Context

Junyi Ao, Dekun Chen, Xiaohai Tian +6

Large Language Models (LLMs) have recently shown remarkable ability to process not only text but also multimodal inputs such as speech and audio. However, most existing models prim…

eess.AS2025

QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions

Siyin Wang, Wenyi Yu, Xianzhao Chen +7

This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical…