Robot speech: how variability matters for child-robot interactions.

Spoken language is one of the most powerful tools for humans to learn, exchange information, and build social relationships. An inherent feature of spoken language is large within- and between-speaker variation across linguistic levels, from sound acoustics to prosodic, lexical, syntactic, and pragmatic choices that differ from written language. Despite advancements in text-to-speech and language models used in social robots, synthetic speech lacks human-like variability. This limitation is especially critical in interactions with children, whose developmental needs require adaptive speech input and ethically responsible design. In child-robot interaction research, robot speech design has received less attention than appearance or multimodal features. We argue that speech variability in robots needs closer examination, considering both how humans adapt to robot speech and how robots could adjust to human speech. We discuss three tensions: (1) feasibility, because dynamic human speech variability is technically challenging to model; (2) desirability, because variability may both enhance and hinder learning, usability, and trust; and (3) ethics, because digital human-like speech risks deception, while robot speech varieties may support transparency. We suggest approaching variability as a design tool while being transparent about the robot's role and capabilities. The key question is which types of variation benefit children's socio-cognitive and language learning, at which developmental stage, in which context, depending on the robot's role and persona. Integrating insights across disciplines, we outline directions for studying how specific dimensions of variability affect comprehension, engagement, language learning, and for developing vocal interactivity that is engaging, ethically transparent, and developmentally appropriate.