38 citations · 88 across the 46 of their papers we have counts for
10 papers · 1 filter
Towards Effective and Efficient Non-autoregressive decoders for Conformer and LLM-based ASR using Block-based Attention Mask
Tianzi Wang, Xurong Xie, Zengrui Jin +9
Automatic speech recognition (ASR) systems often rely on autoregressive (AR) Transformer decoder architectures, which limit efficient inference parallelization due to their sequent…
Augmenting Open-Vocabulary Dysarthric Speech Assessment with Human Perceptual Supervision
Kaimeng Jia, Minzhu Tu, Zengrui Jin +2
Dysarthria is a speech disorder characterized by impaired intelligibility and reduced communicative effectiveness. Automatic dysarthria assessment provides a scalable, cost-effecti…
Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Siyin Wang, Zengrui Jin, Changli Tang +26
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…
Speech-Audio Compositional Attacks on Multimodal LLMs and Their Mitigation with SALMONN-Guard
Yudong Yang, Xuezhen Zhang, Zhifeng Han +6
Recent progress in LLMs has enabled understanding of audio signals, but has also exposed new safety risks arising from complex audio inputs that are inadequately handled by current…
Multiplexing Neural Audio Watermarks with Adaptive Routing
Zheqi Yuan, Yucheng Huang, Guangzhi Sun +2
Audio watermarking supports speech authenticity verification. We study whether combining heterogeneous watermarks can retain at least one detectable provenance signal when their fa…
SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations
Xiaoyu Yang, Yifan Yang, Zengrui Jin +5
Self-supervised learning (SSL) has significantly advanced acoustic representation learning. However, most existing models are optimised for either speech or audio event understandi…
Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing
Mengqi Wang, Zhan Liu, Zengrui Jin +3
Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on…
Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision
Zhaoqing Li, Haoning Xu, Zengrui Jin +7
Model compression has become an emerging need as the sizes of modern speech systems rapidly increase. In this paper, we study model weight quantization, which directly reduces the…
Unfolding A Few Structures for The Many: Memory-Efficient Compression of Conformer and Speech Foundation Models
Zhaoqing Li, Haoning Xu, Xurong Xie +3
This paper presents a novel memory-efficient model compression approach for Conformer ASR and speech foundation systems. Our approach features a unique "small-to-large" design. A c…
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models
Haoning Xu, Zhaoqing Li, Zengrui Jin +7
This paper presents a novel mixed-precision quantization approach for speech foundation models that tightly integrates mixed-precision learning and quantized model parameter estima…