Singing speaker clustering based on subspace learning in the GMM mean supervector space-Reference-Cited by-同舟云学术

Singing speaker clustering based on subspace learning in the GMM mean supervector space

Published:2013-06 Issue:5 Volume:55 Page:653-666
ISSN:0167-6393
Container-title:Speech Communication
language:en
Short-container-title:Speech Communication

Author:

Mehrabani Mahnoosh,Hansen John H.L.

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modeling and Simulation,Software

Reference41 articles.

1. Laplacian eigenmaps and spectral techniques for embedding and clustering;Belkin;Adv. Neural Inf. Process. Systems,2002

2. Speaker diarization using bottom-up clustering based on a parameter-derived distance between adapted gmms;Ben,2004

3. Pattern Recognition with Fuzzy Objective Function Algorithms;Bezdek,1981

4. Neural Networks for Pattern Recognition;Bishop,1995

5. Stress perturbation of neutral speech for synthesis based on hidden markov models;Bou-Ghazale;IEEE Trans. Speech Audio Process.,1998

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