5 citations · 10 across the 13 of their papers we have counts for
9 papers · 1 filter
SpeechSense: A Paralinguistic-Focused Dataset for Fine-Grained Speech Sentiment Analysis
Shicheng Ma, Wenqian Cui, Irwin King
Recent advances in AI have revolutionized speech processing, yet effective speech understanding requires discerning not just what is said, but how it is said. Speech Sentiment Anal…
Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM
Wenqian Cui, Xiao-Hui Li, Daxin Tan +2
Speech large language models (SLMs) are typically built from text large language model (TLM) checkpoints, yet they still suffer from a substantial modality gap. Prior work has main…
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models
He Zhang, Wenqian Cui, Haoning Xu +5
Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex…
TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleaving
Wenqian Cui, Lei Zhu, Xiaohui Li +4
Full-Duplex Speech Language Models (FD-SLMs) are specialized foundation models designed to enable natural, real-time spoken interactions by modeling complex conversational turn-tak…
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation
Zhihan Guo, Jiele Wu, Wenqian Cui +4
Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the Open-ended Long Text Generation (Open-LTG) remain…
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Qichao Wang, Ziqiao Meng, Wenqian Cui +6
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans.…