40 citations · 51 across the 16 of their papers we have counts for
17 papers
DM-ASR: Diarization-aware Multi-speaker ASR with Large Language Models
Li Li, Ming Cheng, Weixin Zhu +3
Multi-speaker automatic speech recognition (ASR) aims to transcribe conversational speech involving multiple speakers, requiring the model to capture not only what was said, but al…
Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings
Li Li, Ming Cheng, Juan Liu +1
This paper proposes a Spatially-Augmented Sequence-to-Sequence Neural Diarization (SA-S2SND) framework, which integrates direction-of-arrival (DOA) cues estimated by SRP-DNN into t…
Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning
Ze Li, Ming Cheng, Ming Li
Large-scale self-supervised Pre-Trained Models (PTMs) have shown significant improvements in the speaker verification (SV) task by providing rich feature representations. In this p…
The DKU System for Multi-Speaker Automatic Speech Recognition in MLC-SLM Challenge
Yuke Lin, Ming Cheng, Ze Li +1
We present the DKU system for Task 2 of the MLC-SLM Challenge, which aims to perform multi-speaker automatic speech recognition directly from raw audio without Oracle speaker label…
Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models
Yuke Lin, Ming Cheng, Ze Li +2
Multi-speaker automatic speech recognition (MS-ASR) faces significant challenges in transcribing overlapped speech, a task critical for applications like meeting transcription and…
Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge
Ming Cheng, Fei Su, Cancan Li +2
This paper describes the speaker diarization system developed for the Multimodal Information-Based Speech Processing (MISP) 2025 Challenge. First, we utilize the Sequence-to-Sequen…
Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation
Ming Cheng, Yuke Lin, Ming Li
This paper proposes a novel Sequence-to-Sequence Neural Diarization (S2SND) framework to perform online and offline speaker diarization. It is developed from the sequence-to-sequen…
Distortion Recovery: A Two-Stage Method for Guitar Effect Removal
Ying-Shuo Lee, Yueh-Po Peng, Jui-Te Wu +3
Removing audio effects from electric guitar recordings makes it easier for post-production and sound editing. An audio distortion recovery model not only improves the clarity of th…
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
Yuke Lin, Ming Cheng, Fulin Zhang +3
In this paper, we provide a large audio-visual speaker recognition dataset, VoxBlink2, which includes approximately 10M utterances with videos from 110K+ speakers in the wild. This…
Robust Wake Word Spotting With Frame-Level Cross-Modal Attention Based Audio-Visual Conformer
Haoxu Wang, Ming Cheng, Qiang Fu +1
In recent years, neural network-based Wake Word Spotting achieves good performance on clean audio samples but struggles in noisy environments. Audio-Visual Wake Word Spotting (AVWW…
Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization
Ming Cheng, Ming Li
Audio-visual learning has demonstrated promising results in many classical speech tasks (e.g., speech separation, automatic speech recognition, wake-word spotting). We believe that…
The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023
Ming Cheng, Weiqing Wang, Xiaoyi Qin +4
This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains voice activity detection, clust…
VoxBlink: A Large Scale Speaker Verification Dataset on Camera
Yuke Lin, Xiaoyi Qin, Guoqing Zhao +4
In this paper, we introduce a large-scale and high-quality audio-visual speaker verification dataset, named VoxBlink. We propose an innovative and robust automatic audio-visual dat…
The DKU Post-Challenge Audio-Visual Wake Word Spotting System for the 2021 MISP Challenge: Deep Analysis
Haoxu Wang, Ming Cheng, Qiang Fu +1
This paper further explores our previous wake word spotting system ranked 2-nd in Track 1 of the MISP Challenge 2021. First, we investigate a robust unimodal approach based on 3D a…
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
Weiqing Wang, Xiaoyi Qin, Ming Cheng +3
This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detectio…
Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction
Ming Cheng, Weiqing Wang, Yucong Zhang +2
Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence…
RWF-2000: An Open Large Scale Video Database for Violence Detection
Ming Cheng, Kunjing Cai, Ming Li
In recent years, surveillance cameras are widely deployed in public places, and the general crime rate has been reduced significantly due to these ubiquitous devices. Usually, thes…