681 papers with code • 2 benchmarks • 1 datasets
These leaderboards are used to track progress in speech-recognition
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition.