标题： 标签感知的语音表示学习用于语言识别 显示英文标题

标题： Label Aware Speech Representation Learning For Language Identification

摘要： 用于非语义任务（如语言识别）的语音表示学习方法要么探索了使用分类器模型的监督嵌入提取方法，要么探索了使用原始数据的自监督表示学习方法。 在本文中，我们提出了一种将自监督表示学习与语言标签信息结合的新型框架，用于预训练任务。 该框架称为标签感知语音表示（LASR）学习，使用基于三元组的目标函数，将语言标签与自监督损失函数相结合。 语音表示进一步针对下游任务进行微调。 语言识别实验在两个公共数据集——FLEURS和Dhwani上进行。 在这些实验中，我们说明所提出的LASR框架在语言识别方面优于最先进的系统。 我们还报告了LASR方法对噪声/缺失标签的鲁棒性分析以及其在多语言语音识别任务中的应用。 显示英文摘要

摘要： 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.