1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.SD2022★ 1 cited
Disentangling speech from surroundings with neural embeddings
Ahmed Omran, Neil Zeghidour, Zalán Borsos +3
We present a method to separate speech signals from noisy environments in the embedding space of a neural audio codec. We introduce a new training procedure that allows our model t…
eess.AS2020
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor +7
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and l…