Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance

• URL: http://arxiv.org/abs/2308.04886v1
• Date: Wed, 9 Aug 2023 11:33:53 GMT
• Title: Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance
• Authors: Sourya Dipta Das, Yash Vadi, Abhishek Unnam, Kuldeep Yadav
• Abstract summary: A deployed dialect classification model can encounter anomalous inputs that differ from the training data distribution. Out-of-distribution detection is a new research area that has received little attention in the context of dialect classification. We propose a simple yet effective unsupervised Mahalanobis distance feature-based method to detect out-of-distribution samples.
• Score: 6.358196724648596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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