Improving Spoken Language Identification with Map-Mix
This addresses a challenging incremental improvement for dialect classification in low-resource settings.
The paper tackles the problem of low-resource dialect classification in spoken language identification, where performance degrades for similar languages. Their Map-Mix data augmentation method improves weighted F1 scores by 2% over baselines and yields better model calibration.
The pre-trained multi-lingual XLSR model generalizes well for language identification after fine-tuning on unseen languages. However, the performance significantly degrades when the languages are not very distinct from each other, for example, in the case of dialects. Low resource dialect classification remains a challenging problem to solve. We present a new data augmentation method that leverages model training dynamics of individual data points to improve sampling for latent mixup. The method works well in low-resource settings where generalization is paramount. Our datamaps-based mixup technique, which we call Map-Mix improves weighted F1 scores by 2% compared to the random mixup baseline and results in a significantly well-calibrated model. The code for our method is open sourced on https://github.com/skit-ai/Map-Mix.