diff --git a/src/transformers/models/gemma3n/feature_extraction_gemma3n.py b/src/transformers/models/gemma3n/feature_extraction_gemma3n.py
index 7dcc4e2c5ca8..a64148e98b41 100644
--- a/src/transformers/models/gemma3n/feature_extraction_gemma3n.py
+++ b/src/transformers/models/gemma3n/feature_extraction_gemma3n.py
@@ -305,13 +305,10 @@ def __call__(
         is_batched_sequence = isinstance(raw_speech, Sequence) and isinstance(raw_speech[0], (np.ndarray, Sequence))
         is_batched = is_batched_numpy or is_batched_sequence
 
-        if is_batched:
-            raw_speech = [np.asarray([rs]).T for rs in raw_speech]
-        elif not is_batched and not isinstance(raw_speech, np.ndarray):
-            raw_speech = np.asarray(raw_speech)
-
-        if not is_batched:  # always return a batch
-            raw_speech = [np.asarray([raw_speech])]
+        # Always return a batch
+        if not is_batched:
+            raw_speech = [raw_speech]
+        raw_speech = [np.asarray([rs]).T for rs in raw_speech]
 
         batched_speech = self.pad(
             BatchFeature({"input_features": raw_speech}),
diff --git a/tests/models/gemma3n/test_feature_extraction_gemma3n.py b/tests/models/gemma3n/test_feature_extraction_gemma3n.py
index 92359040c9d3..fd3db8156e0d 100644
--- a/tests/models/gemma3n/test_feature_extraction_gemma3n.py
+++ b/tests/models/gemma3n/test_feature_extraction_gemma3n.py
@@ -228,6 +228,13 @@ def test_call(self, audio_inputs, test_truncation=False):
             for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
                 self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
 
+    def test_call_unbatched(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_audio = floats_list((1, 800))[0]
+        input_features = feature_extractor(np_audio, return_tensors="np").input_features
+        expected_input_features = feature_extractor([np_audio], return_tensors="np").input_features
+        np.testing.assert_allclose(input_features, expected_input_features)
+
     def test_audio_features_attn_mask_consistent(self):
         # regression test for https://github.com/huggingface/transformers/issues/39911
         # Test input_features and input_features_mask have consistent shape