用于语音到意图应用的多任务学习与胶囊网络
Multitask Learning with Capsule Networks for Speech-to-Intent Applications
AI总结：
本文提出将多任务学习融入胶囊网络，通过正则化迫使胶囊向量包含说话者信息，在说话者无关设置下实现语音到意图的直接映射，以降低用户训练负担并提升困难任务性能。
摘要：
语音控制应用可以对社会产生巨大帮助，尤其是对于身体残障人士。然而，这需要对语音中的各种变化具有鲁棒性。一个从与用户的交互和用户示范中学习的口语理解系统，允许此类系统在不同的环境和不同类型的语音中使用，甚至适用于异常或受损的语音，同时也允许用户选择措辞方式。用户发出命令并通过界面输入其意图，之后模型学习将语音直接映射到正确的动作。由于用户的努力应尽可能低，胶囊网络因其与更深的神经网络相比可能只需要很少的训练数据而引起了关注。在本文中，我们展示了胶囊如何结合多任务学习，当任务困难时，多任务学习通常可以提升模型的性能。基本的胶囊网络将通过一种正则化进行扩展，以在其输出中创建更多结构：它通过将所需信息强制纳入胶囊向量中来学习识别说话者。为此，我们从说话者相关设置转向说话者无关设置。
英文摘要：
Voice controlled applications can be a great aid to society, especially for physically challenged people. However this requires robustness to all kinds of variations in speech. A spoken language understanding system that learns from interaction with and demonstrations from the user, allows the use of such a system in different settings and for different types of speech, even for deviant or impaired speech, while also allowing the user to choose a phrasing. The user gives a command and enters its intent through an interface, after which the model learns to map the speech directly to the right action. Since the effort of the user should be as low as possible, capsule networks have drawn interest due to potentially needing little training data compared to deeper neural networks. In this paper, we show how capsules can incorporate multitask learning, which often can improve the performance of a model when the task is difficult. The basic capsule network will be expanded with a regularisation to create more structure in its output: it learns to identify the speaker of the utterance by forcing the required information into the capsule vectors. To this end we move from a speaker dependent to a speaker independent setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
