Instability of a bi-directional TiFGAN in unsupervised speech representation learning
A major challenge in the application of machine learning in the speech domain is the unavailability of annotated data. Supervised machine learning techniques are highly dependent on the amount of labelled data and the quality of the labels. On the other hand, unsupervised training methods do not req...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2021
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302026 |