Accuracy of MFCC-based speaker recognition in series 60 device
EURASIP Journal on Applied Signal Processing
Real-time speaker identification system
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
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Efficient likelihood evaluation and dynamic Gaussian selection for HMM-based speech recognition
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Comparison of the impact of some Minkowski metrics on VQ/GMM based speaker recognition
Computers and Electrical Engineering
Comparison of clustering methods: A case study of text-independent speaker modeling
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A multi-resolution multi-classifier system for speaker verification
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Real-Time Speaker Verification System Implemented on Reconfigurable Hardware
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In speaker identification, most of the computation originates from the distance or likelihood computations between the feature vectors of the unknown speaker and the models in the database. The identification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing vector quantization (VQ) based speaker identification. We reduce the number of test vectors by pre-quantizing the test sequence prior to matching, and the number of speakers by pruning out unlikely speakers during the identification process. The best variants are then generalized to Gaussian mixture model (GMM) based modeling. We apply the algorithms also to efficient cohort set search for score normalization in speaker verification. We obtain a speed-up factor of 16:1 in the case of VQ-based modeling with minor degradation in the identification accuracy, and 34:1 in the case of GMM-based modeling. An equal error rate of 7% can be reached in 0.84 s on average when the length of test utterance is 30.4 s.