diff --git a/examples/speech_to_text/data_utils.py b/examples/speech_to_text/data_utils.py
index 0d7c03441..fa0d45961 100644
--- a/examples/speech_to_text/data_utils.py
+++ b/examples/speech_to_text/data_utils.py
@@ -126,7 +126,9 @@ def gen_config_yaml(
     specaugment_policy: str = "lb",
     prepend_tgt_lang_tag: bool = False,
     sampling_alpha: float = 1.0,
-    audio_root: str = ""
+    audio_root: str = "",
+    cmvn_type: str = "utterance",
+    gcmvn_path: Optional[Path] = None,
 ):
     manifest_root = manifest_root.absolute()
     writer = S2TDataConfigWriter(manifest_root / yaml_filename)
@@ -151,8 +153,19 @@ def gen_config_yaml(
     if prepend_tgt_lang_tag:
         writer.set_prepend_tgt_lang_tag(True)
     writer.set_sampling_alpha(sampling_alpha)
-    writer.set_feature_transforms("_train", ["utterance_cmvn", "specaugment"])
-    writer.set_feature_transforms("*", ["utterance_cmvn"])
+
+    if cmvn_type not in ["global", "utterance"]:
+        raise NotImplementedError
+
+    writer.set_feature_transforms("_train", [f"{cmvn_type}_cmvn", "specaugment"])
+    writer.set_feature_transforms("*", [f"{cmvn_type}_cmvn"])
+
+    if cmvn_type == "global":
+        assert gcmvn_path is not None, (
+            'Please provide path of global cmvn file.'
+        )
+        writer.set_global_cmvn(gcmvn_path)
+
     if len(audio_root) > 0:
         writer.set_audio_root(audio_root)
     writer.flush()
@@ -206,6 +219,16 @@ def filter_manifest_df(
     return df[valid]
 
 
+def cal_gcmvn_stats(features_list):
+    features = np.concatenate(features_list)
+    square_sums = (features ** 2).sum(axis=0)
+    mean = features.mean(axis=0)
+    features = np.subtract(features, mean)
+    var = square_sums / features.shape[0] - mean ** 2
+    std = np.sqrt(np.maximum(var, 1e-8))
+    return {"mean": mean.astype("float32"), "std": std.astype("float32")}
+
+
 class S2TDataConfigWriter(object):
     DEFAULT_VOCAB_FILENAME = "dict.txt"
     DEFAULT_INPUT_FEAT_PER_CHANNEL = 80
@@ -297,6 +320,9 @@ class S2TDataConfigWriter(object):
     def set_bpe_tokenizer(self, bpe_tokenizer: Dict[str, Any]):
         self.config["bpe_tokenizer"] = bpe_tokenizer
 
+    def set_global_cmvn(self, stats_npz_path: str):
+        self.config["stats_npz_path"] = stats_npz_path
+
     def set_feature_transforms(self, split: str, transforms: List[str]):
         if "transforms" not in self.config:
             self.config["transforms"] = {}
diff --git a/examples/speech_to_text/prep_mustc_data.py b/examples/speech_to_text/prep_mustc_data.py
index 520968401..4e410bcb1 100644
--- a/examples/speech_to_text/prep_mustc_data.py
+++ b/examples/speech_to_text/prep_mustc_data.py
@@ -13,6 +13,7 @@ from itertools import groupby
 from tempfile import NamedTemporaryFile
 from typing import Tuple
 
+import numpy as np
 import pandas as pd
 import torchaudio
 from examples.speech_to_text.data_utils import (
@@ -24,6 +25,7 @@ from examples.speech_to_text.data_utils import (
     get_zip_manifest,
     load_df_from_tsv,
     save_df_to_tsv,
+    cal_gcmvn_stats,
 )
 from torch import Tensor
 from torch.utils.data import Dataset
@@ -111,10 +113,28 @@ def process(args):
             print(f"Fetching split {split}...")
             dataset = MUSTC(root.as_posix(), lang, split)
             print("Extracting log mel filter bank features...")
+            if split == 'train' and args.cmvn_type == "global":
+                print("And estimating cepstral mean and variance stats...")
+                gcmvn_feature_list = []
+
             for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset):
-                extract_fbank_features(
-                    waveform, sample_rate, feature_root / f"{utt_id}.npy"
+                features = extract_fbank_features(waveform, sample_rate)
+
+                np.save(
+                    (feature_root / f"{utt_id}.npy").as_posix(),
+                    features
                 )
+
+                if split == 'train' and args.cmvn_type == "global":
+                    if len(gcmvn_feature_list) < args.gcmvn_max_num:
+                        gcmvn_feature_list.append(features)
+
+            if split == 'train' and args.cmvn_type == "global":
+                # Estimate and save cmv
+                stats = cal_gcmvn_stats(gcmvn_feature_list)
+                with open(cur_root / "gcmvn.npz", "wb") as f:
+                    np.savez(f, mean=stats["mean"], std=stats["std"])
+
         # Pack features into ZIP
         zip_path = cur_root / "fbank80.zip"
         print("ZIPing features...")
@@ -158,6 +178,11 @@ def process(args):
             spm_filename_prefix + ".model",
             yaml_filename=f"config_{args.task}.yaml",
             specaugment_policy="lb",
+            cmvn_type=args.cmvn_type,
+            gcmvn_cmvn_path=(
+                cur_root / "gcmvn.npz" if args.cmvn_type == "global"
+                else None
+            ),
         )
         # Clean up
         shutil.rmtree(feature_root)
@@ -216,6 +241,14 @@ def main():
     parser.add_argument("--vocab-size", default=8000, type=int)
     parser.add_argument("--task", type=str, choices=["asr", "st"])
     parser.add_argument("--joint", action="store_true", help="")
+    parser.add_argument("--cmvn-type", default="utterance",
+                        choices=["global", "utterance"],
+                        help="The type of cepstral mean and variance normalization")
+    parser.add_argument("--gcmvn-max-num", default=150000, type=int,
+                        help=(
+                            "Maximum number of sentences to use to estimate"
+                            "global mean and variance"
+                            ))
     args = parser.parse_args()
 
     if args.joint: