diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 2be43981a64c..a8194a639cb3 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -409,6 +409,8 @@ class CTCLoss(Loss):
         length respectively.
     weight : float or None
         Global scalar weight for loss.
+    blank_label : {'first', 'last'}, default 'last'
+        Set the label that is reserved for blank label.
 
 
     Inputs:
@@ -452,13 +454,16 @@ class CTCLoss(Loss):
         Sequence Data with Recurrent Neural Networks
         <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_
     """
-    def __init__(self, layout='NTC', label_layout='NT', weight=None, **kwargs):
+    def __init__(self, layout='NTC', label_layout='NT', weight=None, blank_label='last', **kwargs):
         assert layout in ['NTC', 'TNC'],\
                "Only 'NTC' and 'TNC' layouts for pred are supported. Got: %s"%layout
         assert label_layout in ['NT', 'TN'],\
                "Only 'NT' and 'TN' layouts for label are supported. Got: %s"%label_layout
+        assert blank_label in ['first', 'last'],\
+                "Only 'first' and 'last' are supported for blank_label. Got: %s"%blank_label
         self._layout = layout
         self._label_layout = label_layout
+        self._blank_label = blank_label
         batch_axis = label_layout.find('N')
         super(CTCLoss, self).__init__(weight, batch_axis, **kwargs)
 
@@ -471,7 +476,7 @@ def hybrid_forward(self, F, pred, label,
         loss = F.contrib.CTCLoss(pred, label, pred_lengths, label_lengths,
                                  use_data_lengths=pred_lengths is not None,
                                  use_label_lengths=label_lengths is not None,
-                                 blank_label='last')
+                                 blank_label=self._blank_label)
         return _apply_weighting(F, loss, self._weight, sample_weight)