面向說話人日志的多原型驅動圖神經網絡方法
打開文本圖片集
Multi-prototype driven graph neural network for speaker diarization
Abstract：Recently，theutilizationof graphneuralnetwork forsesson-levelmodelinghasdemonstrateditseficacyforspeakerdiarization.However，mostof existing variantssolelyrelyonlocalstructure information，gnoringtheimportanceof global speakerinformation，whichcannotfullycompensateforthelackof speakerinformationinthespeakerdiarizationtask.This paper proposedamulti-prototypedriven graphneuralnetwork（MPGNN）forrepresentationlearning，whichefectivelycombined local and global speaker information within each session and simultaneously remaps X -vector to a new embedding space that was moresuitableforclustering.Specifically，，the designof prototypelearning withadynamicandadaptive approach wasacritical component，where more accurateglobal speaker informationcould becaptured.Experimentalresultsshowthatthe proposed MPGNN approach significantly outperforms the baseline systems，achieving diarization error rates（DER）of 3.33% ， 3.52% ， （204號 5.66% ，and 6.52% on the AMI_SDM and CALLHOME datasets respectively.
Keywords：speakerdiarization；graphneural network；local structure information；global speaker information；multiprototype learning
0 引言
說話人日志（speakerdiarization，SD）的目標是解決“誰在何時說話”的問題，即在給定的包含多個說話人交流的長音頻信號中，同時實現說話人識別和說話人定位。（剩余15780字）