Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario

Abstract

Domain mismatch is a critical issue when it comes to spoken language identification. To overcome the domain mismatch problem, we have applied several architectures and deep learning strategies which have shown good results in cross-domain speaker verification tasks to spoken language identification. Our systems were evaluated on the Oriental Language Recognition (OLR) Challenge 2021 Task 1 dataset, which provides a set of cross-domain language identification trials. Among our experimented systems, the best performance was achieved by using the mel frequency cepstral coefficient (MFCC) and pitch features as input and training the ECAPA-TDNN system with a flow-based regularization technique, which resulted in a Cavg of 0.0631 on the OLR 2021 progress set.

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