A Novel Speech to Mouth Articulation System for Realistic Humanoid Robots

Abstract

A significant ongoing issue in realistic humanoid robotics (RHRs) is inaccurate speech to mouth synchronisation. Even the most advanced robotic systems cannot authentically emulate the natural movements of the human jaw, lips and tongue during verbal communication. These visual and functional irregularities have the potential to propagate the Uncanny Valley Effect (UVE) and reduce speech understanding in human-robot interaction (HRI). This paper outlines the development and testing of a novel Computer Aided Design (CAD) robotic mouth prototype with buccinator actuators for emulating the fluidic movements of the human mouth. The robotic mouth system incorporates a custom Machine Learning (ML) application that measures the acoustic qualities of speech synthesis (SS) and translates this data into servomotor triangulation for triggering jaw, lip and tongue positions. The objective of this study is to improve current robotic mouth design and provide engineers with a framework for increasing the authenticity, accuracy and communication capabilities of RHRs for HRI. The primary contributions of this study are the engineering of a robotic mouth prototype and the programming of a speech processing application that achieved a 79.4% syllable accuracy, 86.7% lip synchronisation accuracy and 0.1s speech to mouth articulation differential.

Robotic service quality, authenticity, and revisit intention to restaurants in China: extending cognitive appraisal theory

“…Service robots with an appropriate appearance can improve customers' perceptions of service quality and authenticity (Strathearn and Ma, 2021;Zhu and Chang, 2020). By appearance, service robots have been categorised into human-like, machine-like, and mascotlike robots (Zhang et al, 2021).…”