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Kernel Alignment Maximization for Speaker Recognition Based on High-Level Features

In this paper text-independent automatic speaker verification based on support vector machines is considered. A generalized linear kernel training method based on kernel alignment maximization is proposed. First, kernel matrix decomposition into a sum of maximally aligned directions in the input space is performed and this decomposition is spectrally optimized. The method was evaluated for high-level speaker features: prosodic, articulatory and lexical. The experiments were undertaken employing Switchboard corpus. The proposed algorithm gave equal error rate (EER) reduction up to 23%.