1 code implementation • 24 Oct 2023 • Robert Flynn, Anton Ragni
For the task of speech recognition, the use of more than 30 seconds of acoustic context during training is uncommon, and under-investigated in literature.
no code implementations • 29 Jun 2023 • Robert Flynn, Anton Ragni
While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context.
1 code implementation • 25 May 2023 • Sebastian Vincent, Robert Flynn, Carolina Scarton
This work introduces MTCue, a novel neural machine translation (NMT) framework that interprets all context (including discrete variables) as text.
no code implementations • SEMEVAL 2021 • Robert Flynn, Matthew Shardlow
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale.