Search Results for author:
Found 4 papers, 1 papers with code
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low-resource speech recognition has been long-suffering from insufficient training data.
Hybrid Attentional Memory Network for Computational drug repositioning
Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations.
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection
Moreover, based on the speech collection, we proposed a neural network-based frame-by-frame mapping method to recover the speech content by converting from the adversarial speech to the human speech.
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling
The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
