Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis
  Distance
Sourya Dipta Das Yash Vadi Abhishek Unnam Kuldeep Yadav
Dialect classification is used in a variety of applications, such as machine
translation and speech recognition, to improve the overall performance of the
system. In a real-world scenario, a deployed dialect classification model can
encounter anomalous inputs that differ from the training data distribution,
also called out-of-distribution (OOD) samples. Those OOD samples can lead to
unexpected outputs, as dialects of those samples are unseen during model
training. Out-of-distribution detection is a new research area that has
received little attention in the context of dialect classification. Towards
this, we proposed a simple yet effective unsupervised Mahalanobis distance
feature-based method to detect out-of-distribution samples. We utilize the
latent embeddings from all intermediate layers of a wav2vec 2.0
transformer-based dialect classifier model for multi-task learning. Our
proposed approach outperforms other state-of-the-art OOD detection methods
significantly.