# How to understand a equation related to speaker recognition?

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.

$$N_z$$ is number of frames in the utterance, it is exactly time dimension. Instead "number of features" they should say "number of feature vectors".