Enhancing Usability of Voice Interfaces for Socially Assistive Robots Through Deep Learning: A German Case Study

Voice Interfaces have become ubiquitous as they can make complex technology more usable and accessible. Current voice interfaces, however, often require the user to learn specific speech commands or sentence patterns to use them. This property does not satisfy usability heuristics and causes current language interfaces to underachieve the naturalness of language interaction. To address this issue, we developed a voice interface that is capable of understanding natural everyday language. The overall objective is to build a German language voice interface for socially assistive robots that can work in public spaces. Therefore, we cannot assume the user’s prior knowledge or experience. Based on recent advances in deep natural language processing, we have built a voice interface that is not restricted to specific speech commands. To test this voice interface, we conducted a study with 47 participants. Results indicate 93% of the given tasks were solved successfully by the target user group without prior training or experience with the voice interface.