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We propose an online session-adaptation algorithm for making variable-text speaker-recognition systems robust to inter-session variability, by a continuous update of registered speakers' multiple templates from test utterances during actual usage of the system. The algorithm is set in a speaker-verification mode of operation and uses an enhanced verification step to ensure reliable selection of input...
We present a generic method for analyzing the effect of process variability in nanoscale circuits. The proposed framework uses kernel and a generic tail probability estimator to eliminate the need for a-priori density choice for the nature of circuit variation. This allows capturing the true nature of the circuit variation from a few random samples of its observed responses. The data-driven, non-parametric,...
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