I Summary of Best Ti and Td Vq Performance Speaker Recognition Using Hidden Markov Models, Dynamic Time Warping and Vector Quantisation

@inproceedings{Mason1995ISO,
  title={I Summary of Best Ti and Td Vq Performance Speaker Recognition Using Hidden Markov Models, Dynamic Time Warping and Vector Quantisation},
  author={John L. Mason},
  year={1995}
}
1 Illustration of the segmentation of the database collected over a period of three months into training and 3 %Error against total number of mixtures for TI ergodic CDHMMs (10 version training) 7 %Error against the number of training versions for a TI 32 element VQ, and 32 mixture single state CDHMM 11 8 %Error against the number of training versions for… CONTINUE READING