| language: "en" | |
| thumbnail: | |
| tags: | |
| - Spoken language understanding | |
| license: "CC0" | |
| datasets: | |
| - Timers and Such | |
| metrics: | |
| - Accuracy | |
| # 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`. | |
| #### Referencing SpeechBrain | |
| ``` | |
| @misc{SB2021, | |
| author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, | |
| title = {SpeechBrain}, | |
| year = {2021}, | |
| publisher = {GitHub}, | |
| journal = {GitHub repository}, | |
| howpublished = {\\\\\\\\url{https://github.com/speechbrain/speechbrain}}, | |
| } | |
| ``` | |
| #### Referencing Timers and Such | |
| (TODO add paper once released) |