This is the detailed system description of the IITKGP-ABSP lab's submission to the NIST language recognition evaluation (LRE) 2022. The objective of this LRE (LRE22) is focused on recognizing 14 low-resourced African languages. Even though NIST has provided additional training and development data, we develop our systems with additional constraints of extreme low-resource. Our primary fixed-set submission ensures the usage of only the LRE 22 development data that contains the utterances of 14 target languages. We further restrict our system from using any pre-trained models for feature extraction or classifier fine-tuning. To address the issue of low-resource, our system relies on diverse audio augmentations followed by classifier fusions. Abiding by all the constraints, the proposed methods achieve an EER of 11.43% and cost metric of 0.41 in the LRE22 development set. For users with limited computational resources or limited storage/network capabilities, the proposed system will help achieve efficient LID performance.

翻译：本文详细描述了IITKGP-ABSP实验室提交至美国国家标准与技术研究院（NIST）2022年语言识别评测（LRE）的系统方案。本次LRE（LRE22）的核心目标是识别14种低资源非洲语言。尽管NIST提供了额外的训练和开发数据，我们仍在极端低资源的附加约束下开发了系统。我们的主要固定集提交方案确保仅使用包含14种目标语言语音片段的LRE22开发数据。我们进一步限制系统使用任何预训练模型进行特征提取或分类器微调。为解决低资源问题，我们的系统依赖于多样化的音频增强技术及后续的分类器融合策略。在遵守所有约束条件的前提下，所提出的方法在LRE22开发集上实现了11.43%的等错误率（EER）和0.41的代价指标。对于计算资源有限或存储/网络能力受限的用户，本系统将有助于实现高效的语言识别性能。