1 citations · 1 across the 4 of their papers we have counts for
7 papers
VocalBench-zh: Decomposing and Benchmarking the Speech Conversational Abilities in Mandarin Context
Heyang Liu, Ziyang Cheng, Yuhao Wang +6
The development of multi-modal large language models (LLMs) leads to intelligent approaches capable of speech interactions. As one of the most widely spoken languages globally, Man…
VocalNet-M2: Advancing Low-Latency Spoken Language Modeling via Integrated Multi-Codebook Tokenization and Multi-Token Prediction
Yuhao Wang, Ziyang Cheng, Heyang Liu +4
Current end-to-end spoken language models (SLMs) have made notable progress, yet they still encounter considerable response latency. This delay primarily arises from the autoregres…
When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs
Hongcheng Liu, Pingjie Wang, Yuhao Wang +3
Multimodal large language models (MLLMs) have shown strong capabilities across a broad range of benchmarks. However, most existing evaluations focus on passive inference, where mod…
VocalBench-DF: A Benchmark for Evaluating Speech LLM Robustness to Disfluency
Hongcheng Liu, Yixuan Hou, Heyang Liu +3
While Speech Large Language Models (Speech-LLMs) show strong performance in many applications, their robustness is critically under-tested, especially to speech disfluency. Existin…
CS3-Bench: Evaluating and Enhancing Speech-to-Speech LLMs for Mandarin-English Code-Switching
Heyang Liu, Yuhao Wang, Ziyang Cheng +4
The advancement of multimodal large language models has accelerated the development of speech-to-speech interaction systems. While natural monolingual interaction has been achieved…
VocalNet: Speech LLM with Multi-Token Prediction for Faster and High-Quality Generation
Yuhao Wang, Heyang Liu, Ziyang Cheng +4
Speech large language models (LLMs) have emerged as a prominent research focus in speech processing. We introduce VocalNet-1B and VocalNet-8B, a series of high-performance, low-lat…