论文标题
来自关键字的话语级意图识别
Utterance-level Intent Recognition from Keywords
论文作者
论文摘要
本文重点介绍有关计算和内存有限的平台的意图（WOI）技术。我们的话语水平意图分类的方法基于发声中的一系列关键字，而不是单个固定的键短语。关键字序列转变为四种输入功能，即声音，手机，Word2Vec和Speech2Vec，用于个人意图学习，然后融合决策。如果检测到尾流的意图，则将触发后来的ASR。该系统在英特尔（Amie）新收集的内部数据集上进行了训练和测试，该数据集将首次在本文中进行报告。证明，我们的新技术用钥匙短语的代表成功地在包括车内人机通信在内的不同领域成功实现了强大的意图分类。意图系统的唤醒将是低功率和低复杂性，这使其适合在现实生活中的基于硬件的应用程序中始终进行操作。
This paper focuses on wake on intent (WOI) techniques for platforms with limited compute and memory. Our approach of utterance-level intent classification is based on a sequence of keywords in the utterance instead of a single fixed key phrase. The keyword sequence is transformed into four types of input features, namely acoustics, phones, word2vec and speech2vec for individual intent learning and then fused decision making. If a wake intent is detected, it will trigger the power-costly ASR afterwards. The system is trained and tested on a newly collected internal dataset in Intel called AMIE, which will be reported in this paper for the first time. It is demonstrated that our novel technique with the representation of the key-phrases successfully achieved a noise robust intent classification in different domains including in-car human-machine communications. The wake on intent system will be low-power and low-complexity, which makes it suitable for always on operations in real life hardware-based applications.
