Combining Matching Scores in Identification Model
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Combined speech recognition and speaker verification over the fixed and mobile telephone networks
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Classification Methods for Speaker Recognition
Speaker Classification I
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Speaker Classification II
Activity-aware ECG-based patient authentication for remote health monitoring
Proceedings of the 2009 international conference on Multimodal interfaces
Rejection of non-meaningful activities for HMM-based activity recognition system
Image and Vision Computing
Effects of long-term ageing on speaker verification
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Proceedings of the 2nd Conference on Wireless Health
Speaker verification in score-ageing-quality classification space
Computer Speech and Language
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Likelihood ratio or cohort normalized scoring has been shown to be effective for improving the performance of speaker verification systems. An important problem in this connection is the establishment of principles for constructing speaker background or cohort models which provide the most effective normalized scores. Several kinds of speaker background models are studied. These include individual speaker models, models constructed from the pooled utterances of different numbers of speakers, models selected on the basis of similarity with customer models, models constructed from random selections of speakers, and models constructed from databases recorded under different conditions than the customer models. The results of experiments show that pooled models based on similarity to the reference speaker perform better than individual cohort models from the same similar set of speakers. Pooled background models from a small number of speakers based on similarity perform about the best, but not significantly better than a random selection of 40 or more gender balanced speakers with training conditions matched to the reference speakers.