A Sequence Kernel and its Application to Speaker Recognition

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 and a 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.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: W. M. Campbell
• Keywords: Explicit Expansion, Explicit Expansion Kernel, Mean-squared Error Training, NIPS 2001, NIPS 2007