Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant analysis in correlation similarity measure space
Proceedings of the 24th international conference on Machine learning
Correlation Metric for Generalized Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Audio, Speech, and Language Processing
Multistage speaker diarization of broadcast news
IEEE Transactions on Audio, Speech, and Language Processing
An overview of automatic speaker diarization systems
IEEE Transactions on Audio, Speech, and Language Processing
A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Semi-supervised speaker clustering refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. In the form of an independent training set, the prior knowledge helps us learn a speaker-discriminative feature transformation, a universal speaker prior model, and a discriminative speaker subspace, or equivalently a speaker-discriminative distance metric. The directional scattering patterns of Gaussian mixture model mean supervectors motivate us to perform discriminant analysis on the unit hypersphere rather than in the Euclidean space, which leads to a novel dimensionality reduction technique called spherical discriminant analysis (SDA). Our experiment results show that in the SDA subspace, speaker clustering yields superior performance than that in other reduced-dimensional subspaces (e.g., PCA and LDA).