# Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain

## TL;DR

This paper introduces a domain attentive fusion method for end-to-end dialect identification that maintains high performance across unknown target domains without prior domain knowledge, tested on diverse broadcast and YouTube data.

## Contribution

The study proposes a novel domain attentive fusion approach that enhances end-to-end dialect identification robustness in domain-mismatched scenarios without needing target domain information.

## Key findings

- Significant performance improvements over traditional methods.
- Effective on broadcast and YouTube data from multiple domains.
- Robustness to unknown target domains demonstrated.

## Abstract

End-to-end deep learning language or dialect identification systems operate on the spectrogram or other acoustic feature and directly generate identification scores for each class. An important issue for end-to-end systems is to have some knowledge of the application domain, because the system can be vulnerable to use cases that were not seen in the training phase; such a scenario is often referred to as a domain mismatched condition. In general, we assume that there is enough variation in the training dataset to expose the system to multiple domains. In this work, we study how to best make use a training dataset in order to have maximum effectiveness on unknown target domains. Our goal is to process the input without any knowledge of the target domain while preserving robust performance on other domains as well. To accomplish this objective, we propose a domain attentive fusion approach for end-to-end dialect/language identification systems. To help with experimentation, we collect a dataset from three different domains, and create experimental protocols for a domain mismatched condition. The results of our proposed approach, which were tested on a variety of broadcast and YouTube data, shows significant performance gain compared to traditional approaches, even without any prior target domain information.