  Speech representation learning approaches for non-semantic tasks such as
language recognition have either explored supervised embedding extraction
methods using a classifier model or self-supervised representation learning
approaches using raw data. In this paper, we propose a novel framework of
combining self-supervised representation learning with the language label
information for the pre-training task. This framework, termed as Label Aware
Speech Representation (LASR) learning, uses a triplet based objective function
to incorporate language labels along with the self-supervised loss function.
The speech representations are further fine-tuned for the downstream task. The
language recognition experiments are performed on two public datasets - FLEURS
and Dhwani. In these experiments, we illustrate that the proposed LASR
framework improves over the state-of-the-art systems on language
identification. We also report an analysis of the robustness of LASR approach
to noisy/missing labels as well as its application to multi-lingual speech
recognition tasks.

Computation and Language;Machine Learning;Sound;Audio and Speech Processing