KR A1: Design and Preliminary Evaluation of a Humanoid-Robot Interaction Prototype Integrating Personalized Voice Synthesis, Contextual Dialogue, and Emotion-Aware Response Generation

원문 초록

This paper presents the design and preliminary evaluation of KR A1, a humanoid-robot interaction prototype that integrates personalized voice synthesis, contextual dialogue processing, and emotion-aware response generation for potential mental-healthcare applications. The system architecture combines a locally hosted large language model (Qwen2.5-3B-Instruct, served via Ollama) for natural-language understanding and response generation, OpenVoice v2 for zero-shot speaker-adaptive speech synthesis, and a two-tier emotion-processing pipeline comprising a keyword heuristic and LLM-based inference. Face recognition (InsightFace) and vector memory retrieval (FAISS with sentence-transformer embeddings) enable user-personalized interaction. The experimental evaluation reported here is limited to the voice-cloning component: cloned voices were evaluated against reference recordings using eight acoustic similarity measures - mel-frequency cepstral coefficients, pitch, spectral centroid, spectral contrast, spectral rolloff, zero-crossing rate, formants, and speaker-embedding cosine similarity - supplemented by a same-speaker versus cross-speaker baseline comparison. All tests were conducted on a small, informal development sample; no statistical significance testing against a held-out baseline was performed. The emotion-recognition accuracy, end-to-end response latency, and therapeutic or clinical impact of the integrated system have not yet been formally evaluated and are identified as directions for future work. All testing was informal and self-conducted by the development team; no patients or external human participants were involved, and the system has not been evaluated or deployed in a clinical setting.