diff --git a/deep_speech_2/README.md b/deep_speech_2/README.md
index 3b20bf4944..3010c0e536 100644
--- a/deep_speech_2/README.md
+++ b/deep_speech_2/README.md
@@ -38,7 +38,13 @@ python datasets/librispeech/librispeech.py --help
 python compute_mean_std.py
 ```
 
-`python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing.
+It will compute mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. The default feature of audio data is power spectrum, and the mfcc feature is also supported. To train and infer based on mfcc feature, please generate this file by
+
+```
+python compute_mean_std.py --specgram_type mfcc
+```
+
+and specify ```--specgram_type mfcc``` when running train.py, infer.py, evaluator.py or tune.py.
 
 More help for arguments:
 
diff --git a/deep_speech_2/compute_mean_std.py b/deep_speech_2/compute_mean_std.py
index 9c301c93f6..0cc84e7302 100644
--- a/deep_speech_2/compute_mean_std.py
+++ b/deep_speech_2/compute_mean_std.py
@@ -10,6 +10,12 @@
 
 parser = argparse.ArgumentParser(
     description='Computing mean and stddev for feature normalizer.')
+parser.add_argument(
+    "--specgram_type",
+    default='linear',
+    type=str,
+    help="Feature type of audio data: 'linear' (power spectrum)"
+    " or 'mfcc'. (default: %(default)s)")
 parser.add_argument(
     "--manifest_path",
     default='datasets/manifest.train',
@@ -39,7 +45,7 @@
 
 def main():
     augmentation_pipeline = AugmentationPipeline(args.augmentation_config)
-    audio_featurizer = AudioFeaturizer()
+    audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
 
     def augment_and_featurize(audio_segment):
         augmentation_pipeline.transform_audio(audio_segment)
diff --git a/deep_speech_2/data_utils/featurizer/audio_featurizer.py b/deep_speech_2/data_utils/featurizer/audio_featurizer.py
index 4b4d02c60f..271e535b6a 100644
--- a/deep_speech_2/data_utils/featurizer/audio_featurizer.py
+++ b/deep_speech_2/data_utils/featurizer/audio_featurizer.py
@@ -6,13 +6,15 @@
 import numpy as np
 from data_utils import utils
 from data_utils.audio import AudioSegment
+from python_speech_features import mfcc
+from python_speech_features import delta
 
 
 class AudioFeaturizer(object):
     """Audio featurizer, for extracting features from audio contents of
     AudioSegment or SpeechSegment.
 
-    Currently, it only supports feature type of linear spectrogram.
+    Currently, it supports feature types of linear spectrogram and mfcc.
 
     :param specgram_type: Specgram feature type. Options: 'linear'.
     :type specgram_type: str
@@ -20,9 +22,10 @@ class AudioFeaturizer(object):
     :type stride_ms: float
     :param window_ms: Window size (in milliseconds) for generating frames.
     :type window_ms: float
-    :param max_freq: Used when specgram_type is 'linear', only FFT bins
+    :param max_freq: When specgram_type is 'linear', only FFT bins
                      corresponding to frequencies between [0, max_freq] are
-                     returned.
+                     returned; when specgram_type is 'mfcc', max_feq is the
+                     highest band edge of mel filters.
     :types max_freq: None|float
     :param target_sample_rate: Audio are resampled (if upsampling or
                                downsampling is allowed) to this before
@@ -91,6 +94,9 @@ def _compute_specgram(self, samples, sample_rate):
             return self._compute_linear_specgram(
                 samples, sample_rate, self._stride_ms, self._window_ms,
                 self._max_freq)
+        elif self._specgram_type == 'mfcc':
+            return self._compute_mfcc(samples, sample_rate, self._stride_ms,
+                                      self._window_ms, self._max_freq)
         else:
             raise ValueError("Unknown specgram_type %s. "
                              "Supported values: linear." % self._specgram_type)
@@ -142,3 +148,39 @@ def _specgram_real(self, samples, window_size, stride_size, sample_rate):
         # prepare fft frequency list
         freqs = float(sample_rate) / window_size * np.arange(fft.shape[0])
         return fft, freqs
+
+    def _compute_mfcc(self,
+                      samples,
+                      sample_rate,
+                      stride_ms=10.0,
+                      window_ms=20.0,
+                      max_freq=None):
+        """Compute mfcc from samples."""
+        if max_freq is None:
+            max_freq = sample_rate / 2
+        if max_freq > sample_rate / 2:
+            raise ValueError("max_freq must be greater than half of "
+                             "sample rate.")
+        if stride_ms > window_ms:
+            raise ValueError("Stride size must not be greater than "
+                             "window size.")
+        # compute 13 cepstral coefficients, and the first one is replaced
+        # by log(frame energy)
+        mfcc_feat = mfcc(
+            signal=samples,
+            samplerate=sample_rate,
+            winlen=0.001 * window_ms,
+            winstep=0.001 * stride_ms,
+            highfreq=max_freq)
+        # Deltas
+        d_mfcc_feat = delta(mfcc_feat, 2)
+        # Deltas-Deltas
+        dd_mfcc_feat = delta(d_mfcc_feat, 2)
+        # concat above three features
+        concat_mfcc_feat = [
+            np.concatenate((mfcc_feat[i], d_mfcc_feat[i], dd_mfcc_feat[i]))
+            for i in xrange(len(mfcc_feat))
+        ]
+        # transpose to be consistent with the linear specgram situation
+        concat_mfcc_feat = np.transpose(concat_mfcc_feat)
+        return concat_mfcc_feat
diff --git a/deep_speech_2/data_utils/featurizer/speech_featurizer.py b/deep_speech_2/data_utils/featurizer/speech_featurizer.py
index 26283892e8..a947588db4 100644
--- a/deep_speech_2/data_utils/featurizer/speech_featurizer.py
+++ b/deep_speech_2/data_utils/featurizer/speech_featurizer.py
@@ -11,23 +11,24 @@ class SpeechFeaturizer(object):
     """Speech featurizer, for extracting features from both audio and transcript
     contents of SpeechSegment.
 
-    Currently, for audio parts, it only supports feature type of linear
-    spectrogram; for transcript parts, it only supports char-level tokenizing
-    and conversion into a list of token indices. Note that the token indexing
-    order follows the given vocabulary file.
+    Currently, for audio parts, it supports feature types of linear
+    spectrogram and mfcc; for transcript parts, it only supports char-level
+    tokenizing and conversion into a list of token indices. Note that the
+    token indexing order follows the given vocabulary file.
 
     :param vocab_filepath: Filepath to load vocabulary for token indices
                            conversion.
     :type specgram_type: basestring
-    :param specgram_type: Specgram feature type. Options: 'linear'.
+    :param specgram_type: Specgram feature type. Options: 'linear', 'mfcc'.
     :type specgram_type: str
     :param stride_ms: Striding size (in milliseconds) for generating frames.
     :type stride_ms: float
     :param window_ms: Window size (in milliseconds) for generating frames.
     :type window_ms: float
-    :param max_freq: Used when specgram_type is 'linear', only FFT bins
+    :param max_freq: When specgram_type is 'linear', only FFT bins
                      corresponding to frequencies between [0, max_freq] are
-                     returned.
+                     returned; when specgram_type is 'mfcc', max_freq is the
+                     highest band edge of mel filters.
     :types max_freq: None|float
     :param target_sample_rate: Speech are resampled (if upsampling or
                                downsampling is allowed) to this before
diff --git a/deep_speech_2/data_utils/normalizer.py b/deep_speech_2/data_utils/normalizer.py
index c123d25d20..1f4aae9a09 100644
--- a/deep_speech_2/data_utils/normalizer.py
+++ b/deep_speech_2/data_utils/normalizer.py
@@ -16,7 +16,7 @@ class FeatureNormalizer(object):
     if mean_std_filepath is provided (not None), the normalizer will directly
     initilize from the file. Otherwise, both manifest_path and featurize_func
     should be given for on-the-fly mean and stddev computing.
-    
+
     :param mean_std_filepath: File containing the pre-computed mean and stddev.
     :type mean_std_filepath: None|basestring
     :param manifest_path: Manifest of instances for computing mean and stddev.
diff --git a/deep_speech_2/evaluate.py b/deep_speech_2/evaluate.py
index 00516dcbf0..19eabf4e5a 100644
--- a/deep_speech_2/evaluate.py
+++ b/deep_speech_2/evaluate.py
@@ -86,6 +86,12 @@
     default=500,
     type=int,
     help="Width for beam search decoding. (default: %(default)d)")
+parser.add_argument(
+    "--specgram_type",
+    default='linear',
+    type=str,
+    help="Feature type of audio data: 'linear' (power spectrum)"
+    " or 'mfcc'. (default: %(default)s)")
 parser.add_argument(
     "--decode_manifest_path",
     default='datasets/manifest.test',
@@ -111,6 +117,7 @@ def evaluate():
         vocab_filepath=args.vocab_filepath,
         mean_std_filepath=args.mean_std_filepath,
         augmentation_config='{}',
+        specgram_type=args.specgram_type,
         num_threads=args.num_threads_data)
 
     # create network config
diff --git a/deep_speech_2/infer.py b/deep_speech_2/infer.py
index bb81feac16..8175263027 100644
--- a/deep_speech_2/infer.py
+++ b/deep_speech_2/infer.py
@@ -51,6 +51,12 @@
     default=multiprocessing.cpu_count(),
     type=int,
     help="Number of cpu processes for beam search. (default: %(default)s)")
+parser.add_argument(
+    "--specgram_type",
+    default='linear',
+    type=str,
+    help="Feature type of audio data: 'linear' (power spectrum)"
+    " or 'mfcc'. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -118,6 +124,7 @@ def infer():
         vocab_filepath=args.vocab_filepath,
         mean_std_filepath=args.mean_std_filepath,
         augmentation_config='{}',
+        specgram_type=args.specgram_type,
         num_threads=args.num_threads_data)
 
     # create network config
diff --git a/deep_speech_2/requirements.txt b/deep_speech_2/requirements.txt
index 2ae7d0895a..721fa28110 100755
--- a/deep_speech_2/requirements.txt
+++ b/deep_speech_2/requirements.txt
@@ -2,3 +2,4 @@ wget==3.2
 scipy==0.13.1
 resampy==0.1.5
 https://github.com/kpu/kenlm/archive/master.zip
+python_speech_features
diff --git a/deep_speech_2/train.py b/deep_speech_2/train.py
index 3a2d0cad9e..6481074c6e 100644
--- a/deep_speech_2/train.py
+++ b/deep_speech_2/train.py
@@ -53,6 +53,12 @@
     default=True,
     type=distutils.util.strtobool,
     help="Use sortagrad or not. (default: %(default)s)")
+parser.add_argument(
+    "--specgram_type",
+    default='linear',
+    type=str,
+    help="Feature type of audio data: 'linear' (power spectrum)"
+    " or 'mfcc'. (default: %(default)s)")
 parser.add_argument(
     "--max_duration",
     default=27.0,
@@ -130,6 +136,7 @@ def data_generator():
             augmentation_config=args.augmentation_config,
             max_duration=args.max_duration,
             min_duration=args.min_duration,
+            specgram_type=args.specgram_type,
             num_threads=args.num_threads_data)
 
     train_generator = data_generator()
diff --git a/deep_speech_2/tune.py b/deep_speech_2/tune.py
index 19a2d55951..2fcca48628 100644
--- a/deep_speech_2/tune.py
+++ b/deep_speech_2/tune.py
@@ -50,6 +50,12 @@
     default=multiprocessing.cpu_count(),
     type=int,
     help="Number of cpu processes for beam search. (default: %(default)s)")
+parser.add_argument(
+    "--specgram_type",
+    default='linear',
+    type=str,
+    help="Feature type of audio data: 'linear' (power spectrum)"
+    " or 'mfcc'. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -133,6 +139,7 @@ def tune():
         vocab_filepath=args.vocab_filepath,
         mean_std_filepath=args.mean_std_filepath,
         augmentation_config='{}',
+        specgram_type=args.specgram_type,
         num_threads=args.num_threads_data)
 
     # create network config