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+
+Please refer to <https://icefall.readthedocs.io/en/latest/recipes/timit.html>
+for how to run models in this recipe.
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diff --git a/egs/timit/ASR/RESULTS.md b/egs/timit/ASR/RESULTS.md
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+## Results
+
+### TIMIT training results (Tdnn_LSTM_CTC)
+#### 2021-11-16
+(Mingshuang Luo): Result of https://github.com/k2-fsa/icefall/pull/114
+
+TensorBoard log is available at https://tensorboard.dev/experiment/qhA1o025Q322kO34SlhWzg/#scalars
+
+Pretrained model is available at https://huggingface.co/luomingshuang/icefall_asr_timit_tdnn_lstm_ctc
+
+The best decoding results (PER) are listed below, we got this results by averaging models from epoch 16 to 25, and using `whole-lattice-rescoring` with lm_scale equals to 0.08.
+
+||TEST|
+|--|--|
+|PER| 19.71% |
+
+You can use the following commands to reproduce our results:
+
+```bash
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+
+cd egs/timit/ASR
+./prepare.sh
+
+export CUDA_VISIBLE_DEVICES="0"
+python tdnn_lstm_ctc/train.py --bucketing-sampler True \
+                              --concatenate-cuts False \
+                              --max-duration 200 \
+                              --world-size 1 \
+                              --lang-dir data/lang_phone
+
+python tdnn_lstm_ctc/decode.py --epoch 25 \
+                               --avg 10 \
+                               --max-duration 20 \
+                               --lang-dir data/lang_phone
+```
+
+### TIMIT training results (Tdnn_LiGRU_CTC)
+#### 2021-11-16
+
+(Mingshuang Luo): Result of phone based Tdnn_LiGRU_CTC model.
+
+Pretrained model is available at https://huggingface.co/luomingshuang/icefall_asr_timit_tdnn_ligru_ctc
+
+The best decoding results (PER) are listed below, we got this results by averaging models from epoch 9 to 25, and using `whole-lattice-rescoring` decoding method with lm_scale equals to 0.1.
+
+||TEST|
+|--|--|
+|PER| 17.66% |
+
+You can use the following commands to reproduce our results:
+
+```bash
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+
+cd egs/timit/ASR
+./prepare.sh
+
+export CUDA_VISIBLE_DEVICES="0"
+python tdnn_ligru_ctc/train.py --bucketing-sampler True \
+                              --concatenate-cuts False \
+                              --max-duration 200 \
+                              --world-size 1 \
+                              --lang-dir data/lang_phone
+
+python tdnn_ligru_ctc/decode.py --epoch 25 \
+                               --avg 17 \
+                               --max-duration 20 \
+                               --lang-dir data/lang_phone
+```
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