diff --git a/openwakeword/model.py b/openwakeword/model.py
index 6ae820c..97d1303 100755
--- a/openwakeword/model.py
+++ b/openwakeword/model.py
@@ -227,7 +227,8 @@ class Model():
         """Reset the prediction buffer"""
         self.prediction_buffer = defaultdict(partial(deque, maxlen=30))
 
-    def predict(self, x: np.ndarray, patience: dict = {}, threshold: dict = {}, timing: bool = False):
+    def predict(self, x: np.ndarray, patience: dict = {},
+                threshold: dict = {}, debounce_time: float = 0.0, timing: bool = False):
         """Predict with all of the wakeword models on the input audio frames
 
         Args:
@@ -242,9 +243,11 @@ class Model():
                              model names and the values are the number of frames. Can reduce false-positive
                              detections at the cost of a lower true-positive rate.
                              By default, this behavior is disabled.
-            threshold (dict): The threshold values to use when the `patience` behavior is enabled.
+            threshold (dict): The threshold values to use when the `patience` or `debounce_time` behavior is enabled.
                               Must be provided as an a dictionary where the keys are the
                               model names and the values are the thresholds.
+            debounce_time (float): The time (in seconds) to wait before returning another non-zero prediction
+                                   after a non-zero prediction. Can preven multiple detections of the same wake-word.
             timing (bool): Whether to return timing information of the models. Can be useful to debug and
                            assess how efficiently models are running on the current hardware.
 
@@ -333,16 +336,22 @@ class Model():
                 timing_dict["models"][mdl] = time.time() - model_start
 
         # Update scores based on thresholds or patience arguments
-        if patience != {}:
+        if patience != {} or debounce_time > 0:
             if threshold == {}:
                 raise ValueError("Error! When using the `patience` argument, threshold "
                                  "values must be provided via the `threshold` argument!")
+            if patience != {} and debounce_time > 0:
+                raise ValueError("Error! The `patience` and `debounce_time` arguments cannot be used together!")
             for mdl in predictions.keys():
                 parent_model = self.get_parent_model_from_label(mdl)
                 if parent_model in patience.keys():
                     scores = np.array(self.prediction_buffer[mdl])[-patience[parent_model]:]
                     if (scores >= threshold[parent_model]).sum() < patience[parent_model]:
                         predictions[mdl] = 0.0
+                if debounce_time > 0:
+                    n_frames = int(debounce_time*1000/80)
+                    if (np.array(self.prediction_buffer[mdl])[-n_frames:] >= threshold[parent_model]).sum() > 0:
+                        predictions[mdl] = 0.0
 
         # (optionally) get voice activity detection scores and update model scores
         if self.vad_threshold > 0: