Multilingual Speech-Based Human–Robot Interaction for Personalized Assistive Robotics in Smart Living Environments
Keywords:
human-robot interaction, multilingual speech processing, assistive robotics, smart living environments, natural language understanding, personalization, system architecture, governanceAbstract
Multilingual speech-based human-robot interaction is becoming a foundational capability for personalized assistive robotics in smart living environments. This paper presents a system-level examination of the architectural, infrastructural, and governance dimensions that shape such systems. Rather than focusing narrowly on algorithmic recognition performance, the analysis integrates advances in multilingual speech processing, natural language understanding, user modeling, and ambient intelligence. The discussion begins with the evolving role of speech as an interaction modality and reviews cross-lingual representation learning and transfer methods. It then develops a layered architecture that separates acoustic processing, language understanding, dialogue management, personalization, and smart environment orchestration. The paper emphasizes structural trade-offs between cloud and edge processing, synchronous and asynchronous dialogue, generic multilingual models and domain-specific adaptation, and individual personalization and collective fairness. Robustness concerns are examined through the lens of acoustic variability, code-switching, dialectal diversity, privacy exposure, and model bias. Governance and policy implications are considered in relation to data protection, medical assistive deployment, interoperability standardization, and long-term sustainability. Throughout, case illustrations from smart homes, elder care, rehabilitation, and ambient assisted living are used to highlight deployment realities. The paper concludes that sustainable multilingual assistive robotics depends not only on accuracy but on modular architectures, transparent governance, inclusive data practices, and continuous evaluation across heterogeneous living environments. Future work should integrate participatory design, federated personalization, and formal auditing mechanisms to balance utility, privacy, and fairness.
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