17 citations · 33 across the 6 of their papers we have counts for
12 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization
Dongmei Wang, Xiong Xiao, Naoyuki Kanda +2
This paper describes a speaker diarization model based on target speaker voice activity detection (TS-VAD) using transformers. To overcome the original TS-VAD model's drawback of b…
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio
Naoyuki Kanda, Xiong Xiao, Jian Wu +6
Speaker-attributed automatic speech recognition (SA-ASR) is a task to recognize "who spoke what" from multi-talker recordings. An SA-ASR system usually consists of multiple modules…
Speaker attribution with voice profiles by graph-based semi-supervised learning
Jixuan Wang, Xiong Xiao, Jian Wu +3
Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice prof…
Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
Xiong Xiao, Naoyuki Kanda, Zhuo Chen +10
This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recogniti…
Speaker diarization with session-level speaker embedding refinement using graph neural networks
Jixuan Wang, Xiong Xiao, Jian Wu +3
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a gl…