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Abstract:
A novel approach for comparing sequences of observations using
an explicit-expansion kernel is demonstrated. The kernel is
derived using the assumption of the independence of the sequence
of observations anda mean-squared error training criterion. The
use of an explicit expansion kernel reduces classifier model size
and computation dramatically, resulting in model sizes and
computation one-hundred times smaller in our application. The
explicit expansion also preserves the computational advantages of
an earlier architecture based on mean-squared error training.
Training using standard support vector machine methodology gives
accuracy that significantly exceeds the performance of
state-of-the-art mean-squared error training for a speaker
recognition task.
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