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.

我们提出了一个简单而有效的无监督马氏距离特征方法，在流行的 wav2vec 2.0 变换器式方言分类器模型的所有中间层的潜在嵌入上进行多任务学习，用于检测超出分布的样本，并在 OOD 检测方法方面显著优于其他最先进的方法。