- Joel Shor
- Aren Jansen
- Ronnie Zvi Maor
- Oran Lang
- Omry Tuval
- Félix de Chaumont Quitry
- Marco Tagliasacchi
- Ira Shavitt
- Dotan Emanuel
Abstract
The ultimate goal of transfer learning is to enable learning with a small amount of data, by using a strong embedding. While significant progress has been made in the visual and language domains, the speech domain does not have such a universal method. This paper presents a new representation of speech signals based on an unsupervised triplet-loss objective, which outperforms both existing state of the art and other representations on a number of transfer learning tasks in the non-semantic speech domain. The embedding is learned on a publicly available dataset, and it is tested on a variety of low-resource downstream tasks, including personalization tasks and medical domain. The model will be publicly released.
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