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.

本文提出了一种新的 Label Aware Speech Representation (LASR) 方法，将自我监督表示学习与语言标签信息相结合，使用三元组目标函数将语言标签与自我监督损失函数结合起来，并进一步优化语音表示以适用于下游任务，发现该方法比现有的语言识别系统更为有效，并可应用于多语音识别任务。