no code implementations • Findings (NAACL) 2022 • Jesin James, Vithya Yogarajan, Isabella Shields, Catherine Watson, Peter Keegan, Keoni Mahelona, Peter-Lucas Jones
We also show that BiLSTM with pre-trained Māori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Māori language.
no code implementations • 21 Aug 2022 • Jesin James, Isabella Shields, Vithya Yogarajan, Peter J. Keegan, Catherine Watson, Peter-Lucas Jones, Keoni Mahelona
The New Zealand Parliament Hansard debates reports were used to build the database.
1 code implementation • 21 Aug 2022 • Binu Abeysinghe, Jesin James, Catherine I. Watson, Felix Marattukalam
A speech synthesis model trained on a large General American English database was fine-tuned into a New Zealand English voice to identify if the changes in the vowel space of synthetic speech could be seen and heard.