activity
20242026
most citedAdvancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2026

Qwen-Audio-3.0-Gen-Preview Technical Report

Junyu Dai, Xiaoyue Duan, Xinyue Fan +14

Existing single-domain and multi-task audio systems remain limited in directly organizing heterogeneous audio components, ambience, and multiple roles into long-form temporal scene…

eess.AS2026

Qwen-Audio-3.0-TTS: Freely Controllable and Highly Robust Speech Synthesis with Multi-Stage Training Paradigm

Bajian Xiang, Cheng Wen, Han Zhao +12

In this report, we present Qwen-Audio-3.0-TTS, a production-oriented speech synthesis system that jointly advances content consistency, speaker similarity, prosodic naturalness, au…

cs.CL2026

Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models

Bajian Xiang, Tingwei Guo, Xuan Chen +1

Large Speech Language Models (LSLMs) typically operate at high token rates (tokens/s) to ensure acoustic fidelity, yet this results in sequence lengths that far exceed the underlyi…

cs.CL2025

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…

cs.CL2024★ 1 cited

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…