diff --git a/changelog/7316.bugfix.md b/changelog/7316.bugfix.md
new file mode 100644
index 000000000000..aaa0a64b2ab8
--- /dev/null
+++ b/changelog/7316.bugfix.md
@@ -0,0 +1,2 @@
+`SingleStateFeaturizer` checks whether it was trained with `RegexInterpreter` as
+nlu interpreter. If that is the case, `RegexInterpreter` is used during prediction.
diff --git a/rasa/core/featurizers/single_state_featurizer.py b/rasa/core/featurizers/single_state_featurizer.py
index 3b86d4962cd3..0f96128959ce 100644
--- a/rasa/core/featurizers/single_state_featurizer.py
+++ b/rasa/core/featurizers/single_state_featurizer.py
@@ -6,7 +6,7 @@
 
 import rasa.shared.utils.io
 from rasa.shared.core.domain import SubState, State, Domain
-from rasa.shared.nlu.interpreter import NaturalLanguageInterpreter
+from rasa.shared.nlu.interpreter import NaturalLanguageInterpreter, RegexInterpreter
 from rasa.shared.core.constants import PREVIOUS_ACTION, ACTIVE_LOOP, USER, SLOTS
 from rasa.shared.constants import DOCS_URL_MIGRATION_GUIDE
 from rasa.shared.core.trackers import is_prev_action_listen_in_state
@@ -34,15 +34,29 @@ class SingleStateFeaturizer:
     """
 
     def __init__(self) -> None:
+        """Initialize the single state featurizer."""
+        # rasa core can be trained separately, therefore interpreter during training
+        # will be `RegexInterpreter`. If the model is combined with a rasa nlu model
+        # during prediction the interpreter might be different.
+        # If that is the case, we need to make sure to "reset" the interpreter.
+        self._use_regex_interpreter = False
         self._default_feature_states = {}
         self.action_texts = []
 
-    def prepare_from_domain(self, domain: Domain) -> None:
+    def prepare_for_training(
+        self, domain: Domain, interpreter: NaturalLanguageInterpreter
+    ) -> None:
         """Gets necessary information for featurization from domain.
 
         Args:
             domain: An instance of :class:`rasa.shared.core.domain.Domain`.
+            interpreter: The interpreter used to encode the state
         """
+        if isinstance(interpreter, RegexInterpreter):
+            # this method is called during training,
+            # RegexInterpreter means that core was trained separately
+            self._use_regex_interpreter = True
+
         # store feature states for each attribute in order to create binary features
         def convert_to_dict(feature_states: List[Text]) -> Dict[Text, int]:
             return {
@@ -156,6 +170,15 @@ def _extract_state_features(
         interpreter: NaturalLanguageInterpreter,
         sparse: bool = False,
     ) -> Dict[Text, List["Features"]]:
+        # this method is called during both prediction and training,
+        # `self._use_regex_interpreter == True` means that core was trained
+        # separately, therefore substitute interpreter based on some trained
+        # nlu model with default RegexInterpreter to make sure
+        # that prediction and train time features are the same
+        if self._use_regex_interpreter and not isinstance(
+            interpreter, RegexInterpreter
+        ):
+            interpreter = RegexInterpreter()
 
         message = Message(data=sub_state)
         # remove entities from possible attributes
diff --git a/rasa/core/featurizers/tracker_featurizers.py b/rasa/core/featurizers/tracker_featurizers.py
index 1f668db59395..ab0c01ced352 100644
--- a/rasa/core/featurizers/tracker_featurizers.py
+++ b/rasa/core/featurizers/tracker_featurizers.py
@@ -132,7 +132,7 @@ def featurize_trackers(
                 f"to get numerical features for trackers."
             )
 
-        self.state_featurizer.prepare_from_domain(domain)
+        self.state_featurizer.prepare_for_training(domain, interpreter)
 
         trackers_as_states, trackers_as_actions = self.training_states_and_actions(
             trackers, domain
diff --git a/tests/core/test_featurizer.py b/tests/core/test_featurizer.py
index dd3024ac184c..634dafd4cfa3 100644
--- a/tests/core/test_featurizer.py
+++ b/tests/core/test_featurizer.py
@@ -122,7 +122,7 @@ def test_single_state_featurizer_creates_encoded_all_actions():
         action_names=["a", "b", "c", "d"],
     )
     f = SingleStateFeaturizer()
-    f.prepare_from_domain(domain)
+    f.prepare_for_training(domain, RegexInterpreter())
     encoded_actions = f.encode_all_actions(domain, RegexInterpreter())
     assert len(encoded_actions) == len(domain.action_names)
     assert all(
@@ -295,3 +295,24 @@ def test_single_state_featurizer_with_interpreter_state_with_no_action_name(
     assert (
         encoded[ACTIVE_LOOP][0].features != scipy.sparse.coo_matrix([[0, 0, 0, 1]])
     ).nnz == 0
+
+
+def test_single_state_featurizer_uses_regex_interpreter(
+    unpacked_trained_moodbot_path: Text,
+):
+    from rasa.core.agent import Agent
+
+    domain = Domain(
+        intents=[], entities=[], slots=[], templates={}, forms=[], action_names=[],
+    )
+    f = SingleStateFeaturizer()
+    # simulate that core was trained separately by passing
+    # RegexInterpreter to prepare_for_training
+    f.prepare_for_training(domain, RegexInterpreter())
+    # simulate that nlu and core models were manually combined for prediction
+    # by passing trained interpreter to encode_all_actions
+    interpreter = Agent.load(unpacked_trained_moodbot_path).interpreter
+    features = f._extract_state_features({TEXT: "some text"}, interpreter)
+    # RegexInterpreter cannot create features for text, therefore since featurizer
+    # was trained without nlu, features for text should be empty
+    assert not features