This question refers to the following paper:
Support Vector Machines for Speaker and Language Recognition, W. M. Campbell, J. P. Campbell, D. A. Reynolds, E. Singer, P. A. Torres-Carrasquillo, Computer speech and Language 20 (2006) 210-229.
I am trying to implement the algorithm in table 1 and table 2 in page 18. In step 6 of of table 1 they are calculating $b_z^i$ as a mean (or sum) of $b(z_i)$ and number of entries is $N_z$ which they claim to be the number of features.
The question is what is $N_z$ here. As I understand each feature set, which is of dimension $N_z$, has been used to create $b(z_i)$, so what this summation means? One can only sum over time dimension, which has nothing to do $N_z$. $N_z$ is kind of spatial dimension as one time frame of data is converted to features.