Multilayer bootstrap network for unsupervised speaker recognition
We apply multilayer bootstrap network (MBN), a recent proposed unsupervised learning method, to unsupervised speaker recognition. The proposed method first extracts supervectors from an unsupervised universal background model, then reduces the dimension of the high-dimensional supervectors by multilayer bootstrap network, and finally conducts unsupervised speaker recognition by clustering the low-dimensional data. The comparison results with 2 unsupervised and 1 supervised speaker recognition techniques demonstrate the effectiveness and robustness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSpeaker RecognitionSimilar Papers 제목 키워드 기반
Multi-channel Speech Separation Using Deep Embedding Model with Multilayer Bootstrap Networks
Recently, deep clustering (DPCL) based speaker-independent speech separation has drawn much attention, since it needs little speaker prior information. However, it still has much room of improvement, particularly in reve…
ClusteringDeep ClusteringSpeech SeparationUnsupervised model compression for multilayer bootstrap networks
Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a…
Dimensionality ReductionmodelModel CompressionThe Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description
We describe the Phonexia submission for the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21) in the unsupervised speaker verification track. Our solution was very similar to IDLab's winning submission for VoxSRC-2…
ClusteringContrastive LearningSpeaker RecognitionSpeaker VerificationSpeaker-IPL: Unsupervised Learning of Speaker Characteristics with i-Vector based Pseudo-Labels
Iterative self-training, or iterative pseudo-labeling (IPL) -- using an improved model from the current iteration to provide pseudo-labels for the next iteration -- has proven to be a powerful approach to enhance the qua…
Speaker RecognitionSpeaker VerificationDeep topic modeling by multilayer bootstrap network and lasso
Topic modeling is widely studied for the dimension reduction and analysis of documents. However, it is formulated as a difficult optimization problem. Current approximate solutions also suffer from inaccurate model- or d…
ClusteringDimensionality ReductionTopic Models