This RFC white Paper summarizes our progress on the MIT Voice Name System
(VNS) and Huey. The VNS, similar in name and function to the DNS, is a system
to reserve and use "wake words" to activate Artificial Intelligence (AI)
devices. Just like you can say "Hey Siri" to activate Apple's personal
assistant, we propose using the VNS in smart speakers and other devices to
route wake requests based on commands such as "turn off", "open grocery
shopping list" or "271, start flash card review of my computer vision class".
We also introduce Huey, an unambiguous Natural Language to interact with AI
devices. We aim to standardize voice interactions to a universal reach similar
to that of other systems such as phone numbering, with an agreed world-wide
approach to assign and use numbers, or the Internet's DNS, with a standard
naming system, that has helped flourish popular services including the
World-Wide-Web, FTP, and email. Just like these standards are "neutral", we
also aim to endow the VNS with "wake neutrality" so that each participant can
develop its own digital voice. We focus on voice as a starting point to talk to
any IoT object and explain briefly how the VNS may be expanded to other AI
technologies enabling person-to-machine conversations (really
machine-to-machine), including computer vision or neural interfaces. We also
describe briefly considerations for a broader set of standards, MIT Open AI
(MOA), including a reference architecture to serve as a starting point for the
development of a general conversational commerce infrastructure that has
standard "Wake Words", NLP commands such as "Shopping Lists" or "Flash Card
Reviews", and personalities such as Pi or 271. Privacy and security are key
elements considered because of speech-to-text errors and the amount of personal
information contained in a voice sample.

本文介绍了 MIT Voice Name System（VNS）和 Huey，VNS 是一种类似于 DNS 的系统，用于保留和使用唤醒词来激活人工智能设备，并且提出了 Huey，一种无歧义的自然语言与 AI 设备交互的方法，旨在使语音交互能够达到类似于电话号码系统或 Internet DNS 的全球性普及。

MIT 语音命名系统

The MIT Voice Name System

The recently proposed audio-visual scene-aware dialog task paves the way to a
more data-driven way of learning virtual assistants, smart speakers and car
navigation systems. However, very little is known to date about how to
effectively extract meaningful information from a plethora of sensors that
pound the computational engine of those devices. Therefore, in this paper, we
provide and carefully analyze a simple baseline for audio-visual scene-aware
dialog which is trained end-to-end. Our method differentiates in a data-driven
manner useful signals from distracting ones using an attention mechanism. We
evaluate the proposed approach on the recently introduced and challenging
audio-visual scene-aware dataset, and demonstrate the key features that permit
to outperform the current state-of-the-art by more than 20\% on CIDEr.

本文提出了一种基于数据驱动学习的音视频感知对话基准方法，采用注意力机制从海量传感器中区分实用信号和干扰信号，实验结果表明此方法在音视频感知数据集中性能超过现有最先进方法 20% 以上。