SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Lattice-based MLLR for speaker recognition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Compensation of Nuisance Factors for Speaker and Language Recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
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We propose a speaker recognition system based on the acoustic segment modeling technique. It is assumed that the overall sound characteristics for speakers can be covered by a set of acoustic segment models (ASMs) while the ASMs are acoustically-motivated self-organized sound units without imposing any phonetic definitions. These acoustic segment models decode a spoken utterance into a string of segment units and the mean vectors of ASMs based on the unsupervised MAP adaptation are concatenated to represent the characteristics of the specific speaker. Support vector machines are thus applied on these high dimensional feature vectors for speaker recognition. We evaluate the proposed approach in the 2006 NIST Speaker Recognition Evaluation core condition test trials.