diff --git a/egs/gigaspeech/ASR/README.md b/egs/gigaspeech/ASR/README.md
index c469b6cee..5ccbcd9ac 100644
--- a/egs/gigaspeech/ASR/README.md
+++ b/egs/gigaspeech/ASR/README.md
@@ -13,6 +13,8 @@ ln -sfv /path/to/GigaSpeech download/GigaSpeech
 ```
 
 ## Performance Record
-| |Dev|Test|
-|---|---|---|
-|WER |11.92|11.85|
+|     |  Dev  | Test  |
+|-----|-------|-------|
+| WER | 11.93 | 11.86 |
+
+See [RESULTS](/egs/gigaspeech/ASR/RESULTS.md) for details.
diff --git a/egs/gigaspeech/ASR/RESULTS.md b/egs/gigaspeech/ASR/RESULTS.md
new file mode 100644
index 000000000..5c8e8a84f
--- /dev/null
+++ b/egs/gigaspeech/ASR/RESULTS.md
@@ -0,0 +1,49 @@
+## Results
+
+### GigaSpeech BPE training results (Conformer-CTC)
+
+#### 2022-04-06
+
+The best WER, as of 2022-04-06, for the gigaspeech is below
+(using HLG decoding + n-gram LM rescoring + attention decoder rescoring):
+
+|     |  Dev  | Test  |
+|-----|-------|-------|
+| WER | 11.93 | 11.86 |
+
+Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
+| ngram_lm_scale | attention_scale |
+|----------------|-----------------|
+|      0.3       |        1.5      |
+
+
+To reproduce the above result, use the following commands for training:
+
+```
+cd egs/gigaspeech/ASR/conformer_ctc
+./prepare.sh
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
+./conformer_ctc/train.py \
+  --max-duration 120 \
+  --num-workers 1 \
+  --world-size 8 \
+  --exp-dir conformer_ctc/exp_500 \
+  --lang-dir data/lang_bpe_500
+```
+
+and the following command for decoding
+
+```
+./conformer_ctc/decode.py \
+  --epoch 19 \
+  --avg 8 \
+  --method attention-decoder \
+  --num-paths 1000 \
+  --exp-dir conformer_ctc/exp_500 \
+  --lang-dir data/lang_bpe_500 \
+  --max-duration 20 \
+  --num-workers 1
+```
+
+The tensorboard log for training is available at
+<https://tensorboard.dev/experiment/rz63cmJXSK2fV9GceJtZXQ/>