Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge
Journal: arXiv (Cornell University)
Year: 2022

This paper investigates different pretraining approaches to spoken language identification. The paper is based on our submission to the Oriental Language Recognition 2021 Challenge. We participated in two tracks of the challenge: constrained and unconstrained language recognition. For the constrained track, we first trained a Conformer-based encoder-decoder model for multilingual automatic speech recognition (ASR), using the provided training data that had transcripts available. The shared encod