Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A proposed likelihood transformation for speaker verification
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Noise Clustering-Based Speaker Verification
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Complementary DNA microarray image processing based on the fuzzy Gaussian mixture model
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Segmentation of complementary DNA microarray images by wavelet-based Markov random field model
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Clustering of time series data-a survey
Pattern Recognition
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Comparison of clustering methods: A case study of text-independent speaker modeling
Pattern Recognition Letters
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In speaker verification, a claimed speaker's score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker's and the impostors' likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better score which can reduce that error. Experiments performed on the TI46 and the ANDOSL speech corpora show better results for the proposed method.