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.

中文翻译：

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来自关键词的话语级意图识别

本文重点介绍用于计算和内存有限的平台的意图唤醒 (WOI) 技术。我们的话语级意图分类方法基于话语中的一系列关键字，而不是单个固定的关键短语。将关键词序列转化为声学、音素、word2vec和speech2vec四种输入特征，用于个体意图学习，然后融合决策。如果检测到唤醒意图，它将在之后触发耗电巨大的 ASR。该系统在英特尔新收集的名为 AMIE 的内部数据集上进行训练和测试，本文将首次报道该数据集。结果表明，我们使用关键短语表示的新技术成功地实现了包括车内人机通信在内的不同领域的噪声鲁棒意图分类。意图唤醒系统将是低功耗和低复杂性的，这使其适用于现实生活中基于硬件的应用程序中的始终在线操作。