Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain
Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain
复制标题
域关注融合，用于未知目标域的端到端方言识别
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
James R. Glass
中科院分区：
文献类型：
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作者：
Suwon Shon;Ahmed Ali;James R. Glass
端到端深度学习语言或方言识别系统对声谱图或其他声学特征进行操作，并直接为每个类别生成识别分数。端到端系统的一个重要问题是对应用程序域有一定的了解，因为系统可能容易受到在训练阶段没有看到的用例的影响;这种情况通常被称为域不匹配条件。一般来说，我们假设训练数据集中有足够的变化，可以将系统暴露给多个域。在这项工作中，我们研究了如何最好地利用训练数据集，以便在未知的目标域上具有最大的有效性。我们的目标是在不了解目标域的情况下处理输入，同时保持对其他域的鲁棒性能。为了实现这一目标，我们提出了一个域注意融合方法的端到端的方言/语言识别系统。为了帮助实验，我们从三个不同的域收集数据集，并为域不匹配的条件创建实验协议。我们提出的方法，这是在各种广播和YouTube数据进行测试的结果，显示显着的性能增益相比，传统的方法，即使没有任何事先的目标域信息。
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.