Advances in voice technology and voice user interfaces (VUIs) -- such as
Alexa, Siri, and Google Home -- have opened up the potential for many new types
of interaction. However, despite the potential of these devices reflected by
the growing market and body of VUI research, there is a lingering sense that
the technology is still underused. In this paper, we conducted a systematic
literature review of 35 papers to identify and synthesize 127 VUI design
guidelines into five themes. Additionally, we conducted semi-structured
interviews with 15 smart speaker users to understand their use and non-use of
the technology. From the interviews, we distill four design challenges that
contribute the most to non-use. Based on their (non-)use, we identify four
opportunity spaces for designers to explore such as focusing on information
support while multitasking (cooking, driving, childcare, etc), incorporating
users' mental models for smart speakers, and integrating calm design
principles.

通过系统文献综述和半结构化访谈，本研究总结了 127 个声音用户界面设计指南，并提出了四个声音用户界面设计的机会空间以提高用户参与度。

智能音箱设计的挑战与机遇

Challenges and Opportunities for the Design of Smart Speakers

Voice technology has become ubiquitous recently. However, the accuracy, and
hence experience, in different languages varies significantly, which makes the
technology not equally inclusive. The availability of data for different
languages is one of the key factors affecting accuracy, especially in training
of all-neural end-to-end automatic speech recognition systems.
Cross-lingual knowledge transfer and iterative pseudo-labeling are two
techniques that have been shown to be successful for improving the accuracy of
ASR systems, in particular for low-resource languages, like Ukrainian.
Our goal is to train an all-neural Transducer-based ASR system to replace a
DNN-HMM hybrid system with no manually annotated training data. We show that
the Transducer system trained using transcripts produced by the hybrid system
achieves 18% reduction in terms of word error rate. However, using a
combination of cross-lingual knowledge transfer from related languages and
iterative pseudo-labeling, we are able to achieve 35% reduction of the error
rate.

该研究旨在通过跨语言知识转移和迭代伪标注的方法来提高语音识别系统对低资源语言的准确性，结果表明，使用这两种技术，可将错误率降低 35%。