Get It Right: Improving Comprehensibility with Adaptable Speech Expression of a Humanoid Service Robot
Thomas Sievers, Ralf Moeller
TL;DR
The paper addresses making official information more comprehensible for diverse public-service users by adapting content to easy language or translating it into another language using a Pepper humanoid robot. It presents an Android Studio/Kotlin architecture that connects Pepper via its SDK to a knowledge base and translation services, employing SUMM AI for easy-language output and DeepL for translations, with a choice between Listen & Say and Chat dialogue modes and optional animations. The study demonstrates feasibility through a case study in a government customer center and reports positive feedback from staff workshops. The work suggests a generalizable, API-based approach to increase accessibility and trust in public-service robotics, with future work on language models and privacy considerations.
Abstract
As humanoid service robots are becoming more and more perceptible in public service settings for instance as a guide to welcome visitors or to explain a procedure to follow, it is desirable to improve the comprehensibility of complex issues for human customers and to adapt the level of difficulty of the information provided as well as the language used to individual requirements. This work examines a case study using a humanoid social robot Pepper performing support for customers in a public service environment offering advice and information. An application architecture is proposed that improves the intelligibility of the information received by providing the possibility to translate this information into easy language and/or into another spoken language.