Spatial position constraint for unsupervised learning of speech representations.

ABSTRACT: The success of supervised learning techniques for automatic speech processing does not always extend to problems with limited annotated speech. Unsupervised representation learning aims at utilizing unlabelled data to learn a transformation that makes speech easily distinguishable for classification tasks, whereby deep auto-encoder variants have been most successful in finding such representations. This paper proposes a novel mechanism to incorporate geometric position of speech samples within the global structure of an unlabelled feature set. Regression to the geometric position is also added as an additional constraint for the representation learning auto-encoder. The representation learnt by the proposed model has been evaluated over a supervised classification task for limited vocabula

SUBMITTER: Humayun MA 

PROVIDER: S-EPMC8323719 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature