Speech Recognition On Timit
Metrics
Percentage error
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Percentage error |
|---|---|
| the-pytorch-kaldi-speech-recognition-toolkit | 16.3 |
| long-short-term-memory-and-learning-to-learn | 33.2 |
| wav2vec-unsupervised-pre-training-for-speech | 14.7 |
| wav2vec-2-0-a-framework-for-self-supervised | 8.3 |
| the-pytorch-kaldi-speech-recognition-toolkit | 14.5 |
| the-pytorch-kaldi-speech-recognition-toolkit | 14.2 |
| quaternion-convolutional-neural-networks-for-1 | 19.64 |
| the-pytorch-kaldi-speech-recognition-toolkit | 16.0 |
| speech-recognition-with-deep-recurrent-neural | 17.7 |
| the-pytorch-kaldi-speech-recognition-toolkit | 14.9 |
| attention-model-for-articulatory-features | 20.4 |
| light-gated-recurrent-units-for-speech | 16.7 |
| Model 13 | 16.5 |
| the-pytorch-kaldi-speech-recognition-toolkit | 15.9 |
| the-pytorch-kaldi-speech-recognition-toolkit | 16.6 |
| segmental-recurrent-neural-networks-for-end | 17.3 |
| the-pytorch-kaldi-speech-recognition-toolkit | 16.5 |
| attention-based-models-for-speech-recognition | 17.6 |
| Model 19 | 16.7 |
| vq-wav2vec-self-supervised-learning-of-1 | 11.6 |
| light-gated-recurrent-units-for-speech | 14.9 |
| online-and-linear-time-attention-by-enforcing | 20.1 |