  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 encoder of the multilingual ASR model was
then finetuned for the language identification task. For the unconstrained
task, we relied on both externally available pretrained models as well as
external data: the multilingual XLSR-53 wav2vec2.0 model was finetuned on the
VoxLingua107 corpus for the language recognition task, and finally finetuned on
the provided target language training data, augmented with CommonVoice data.
Our primary metric $C_{\rm avg}$ values on the Test set are 0.0079 for the
constrained task and 0.0119 for the unconstrained task which resulted in the
second place in both rankings. In post-evaluation experiments, we study the
amount of target language data needed for training an accurate backend model,
the importance of multilingual pretraining data, and compare different models
as finetuning starting points.
