40 citations · 51 across the 16 of their papers we have counts for
13 papers · 1 filter
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…
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…
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-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…