Update README.md

README.md

@@ -13,7 +13,18 @@ metrics:
 
 # End-to-end SLU model for Timers and Such
 
-Attention-based RNN sequence-to-sequence model for Timers and Such trained on the `train-real` subset. Achieves  86.7% accuracy on `test-real`.
+Attention-based RNN sequence-to-sequence model for [Timers and Such](https://zenodo.org/record/4623772) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`.
+
+The model uses an ASR model trained on LibriSpeech (`speechbrain/asr-crdnn-rnnlm-librispeech`) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. 
+
+The dataset has four intents: `SetTimer`, `SetAlarm`, `SimpleMath`, and `UnitConversion`. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?"
+
+You can try the model on the `math.wav` file included here as follows:
+```
+from speechbrain.pretrained import EndToEndSLU
+slu = EndToEndSLU.from_hparams("speechbrain/slu-timers-and-such-direct-librispeech-asr")
+slu.decode_file("math.wav")
+```
 
 #### Referencing SpeechBrain
 
@@ -24,7 +35,7 @@ title = {SpeechBrain},
 year = {2021},
 publisher = {GitHub},
 journal = {GitHub repository},
-howpublished = {\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
+howpublished = {\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
 }
 ```