2 citations · 3 across the 7 of their papers we have counts for
8 papers
MUB: An Identity-Masked Benchmark for Music Understanding in Multimodal Language Models
Kun Liu, Lyra Tao
Evaluating musical perception in multimodal language models requires separating audio evidence from answers recoverable through metadata or external tools. We present the Music Und…
FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System
Kaituo Xu, Yan Jia, Kai Huang +6
We present FireRedASR2S, a state-of-the-art industrial-grade all-in-one automatic speech recognition (ASR) system. It integrates four modules in a unified pipeline: ASR, Voice Acti…
FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations
Junjie Chen, Yao Hu, Junjie Li +12
Full-duplex voice interaction allows users and agents to speak simultaneously with controllable barge-in, enabling lifelike assistants and customer service. Existing solutions are…
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
Weida Wang, Dongchen Huang, Jiatong Li +32
We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than…
From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning
Haoyu Li, Xuhong Li, Yiming Dong +1
Dataset diversity plays a pivotal role for the successful training of many machine learning models, particularly in the supervised fine-tuning (SFT) stage of large language model (…
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models
Bo Liu, Pengfei Qiao, Minhan Ma +5
Understanding surveillance video content remains a critical yet underexplored challenge in vision-language research, particularly due to its real-world complexity, irregular event…
Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment
Ke Wang, Lei He, Kun Liu +3
Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with…
FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications
Hao-Han Guo, Yao Hu, Kun Liu +6
This work proposes FireRedTTS, a foundation text-to-speech framework, to meet the growing demands for personalized and diverse generative speech applications. The framework compris…