diff --git a/bob/bio/gmm/algorithm/GMM.py b/bob/bio/gmm/algorithm/GMM.py
index 3d9d23eadbe0bd8c4902db8b973a880ecc44a692..bc6c1434ba411571de4d95f643ba944dc635d189 100644
--- a/bob/bio/gmm/algorithm/GMM.py
+++ b/bob/bio/gmm/algorithm/GMM.py
@@ -305,6 +305,7 @@ class GMM(BioAlgorithm, BaseEstimator):
                 max_iter=self.kmeans_training_iterations,
                 init_method="k-means||",
                 init_max_iter=5,
+                random_state=self.init_seed,
             ),
         )
 
diff --git a/bob/bio/gmm/test/data/gmm_enrolled.hdf5 b/bob/bio/gmm/test/data/gmm_enrolled.hdf5
index 4774b226e8122be1a1eae3549a5124e4f1906ada..844c2ce4544009b41632c8f6d76e92d0def748b3 100644
Binary files a/bob/bio/gmm/test/data/gmm_enrolled.hdf5 and b/bob/bio/gmm/test/data/gmm_enrolled.hdf5 differ
diff --git a/bob/bio/gmm/test/data/gmm_projected.hdf5 b/bob/bio/gmm/test/data/gmm_projected.hdf5
index 1d030594936b7b1212db6e12baa4956eec4d5dba..471db141995803ed8f5b56dfaa0c154c3344e71d 100644
Binary files a/bob/bio/gmm/test/data/gmm_projected.hdf5 and b/bob/bio/gmm/test/data/gmm_projected.hdf5 differ
diff --git a/bob/bio/gmm/test/data/gmm_ubm.hdf5 b/bob/bio/gmm/test/data/gmm_ubm.hdf5
index 0349d0612863cbf2c3d1dcefa62c29997edb22d8..1f5c4e91215aa6cc8b27319e6386312581f08e57 100644
Binary files a/bob/bio/gmm/test/data/gmm_ubm.hdf5 and b/bob/bio/gmm/test/data/gmm_ubm.hdf5 differ
diff --git a/bob/bio/gmm/test/test_gmm.py b/bob/bio/gmm/test/test_gmm.py
index 4277ee225b1a15fa28d12aacbde4ccec23ac9fcf..43a252a6528e52329bbbcd96dbb89b2865326fa8 100644
--- a/bob/bio/gmm/test/test_gmm.py
+++ b/bob/bio/gmm/test/test_gmm.py
@@ -28,12 +28,12 @@ import bob.bio.gmm
 
 from bob.bio.base.test import utils
 from bob.bio.gmm.algorithm import GMM
-from bob.learn.em.mixture import GMMMachine
-from bob.learn.em.mixture import GMMStats
+from bob.learn.em import GMMMachine
+from bob.learn.em import GMMStats
 
 logger = logging.getLogger(__name__)
 
-regenerate_refs = False
+regenerate_refs = True
 
 seed_value = 5489
 
@@ -57,8 +57,8 @@ def test_training():
     # Set a small training iteration count
     gmm1 = GMM(
         number_of_gaussians=2,
-        kmeans_training_iterations=1,
-        ubm_training_iterations=1,
+        kmeans_training_iterations=5,
+        ubm_training_iterations=5,
         init_seed=seed_value,
     )
     train_data = utils.random_training_set(
@@ -155,7 +155,7 @@ def test_score():
     )
     probe_data = utils.random_array((20, 45), -5.0, 5.0, seed=seed_value)
 
-    reference_score = 0.593373
+    reference_score = 0.707260
 
     numpy.testing.assert_almost_equal(
         gmm1.score(biometric_reference, probe), reference_score, decimal=5