1 citations · 3 across the 8 of their papers we have counts for
8 papers · 1 filter
-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
Heyang Liu, Jiayi Huang, Wenyang Xiao +8
The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seaml…
LaSR: Context-Aware Speech Recognition via Latent Reasoning
Heyang Liu, Ziyang Cheng, Jiayi Huang +5
Speech recognition in specialized domains requires leveraging contextual or topical information to improve the recognition of domain-specific entities. Speech Large Language Models…
VocalNet-MDM: Accelerating Streaming Speech LLM via Self-Distilled Masked Diffusion Modeling
Ziyang Cheng, Yuhao Wang, Heyang Liu +4
Recent Speech Large Language Models~(LLMs) have achieved impressive capabilities in end-to-end speech interaction. However, the prevailing autoregressive paradigm imposes strict se…
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…
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…