There is frequent noise that affects the performance of voice recog-
nition and speaker recognition algorithms in a real-world envi-
ronment. For voice and speaker recognition systems to be more

robust, these noises must not interfere in a harmful way, causing
errors in understanding commands. To evaluate signal degradation
and speaker recognition when exposed to real-world environments,
we explore a reverberation noise simulation environment using
a specific library in this work. We tested a speaker recognition

model with i-vectors and Probabilistic Linear Discriminant Analy-
sis (PLDA). We have analyzed the impact of noise in conjunction

with reverberation on its error rate. Results based on Monte Carlo
simulation showed that, for the tested cases, the noise set with
reverberation worsened the recognition rate by up to 24,43%.