Towards Learning a Universal Non-Semantic Representation of Speech

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. While significant progress has been made in the visual and language domains, the speech community has yet to identify a strategy with wide-reaching applicability across tasks. This paper describes a representation of speech based on an unsupervised triplet-loss objective, which exceeds state-of-the-art performance on a number of transfer learning tasks drawn from the non-semantic speech domain. The embedding is trained 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.