Drawing upon the Elaboration Likelihood Model (ELM) and Trust Transfer Theory, this study investigates how AI digital avatars influence consumer trust and purchase intention. Using survey data collected from 378 valid respondents, the proposed model was tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that brand awareness and perceived quality significantly affect purchase intention through product trust, while social endorsement cues, anthropomorphism, and interaction quality indirectly influence purchase intention through AI Avatar trust. Furthermore, both AI Avatar trust and product trust have significant direct effects on purchase intention, highlighting the critical role of trust in AI-driven persuasion processes. This study validates the dual-route persuasion mechanism of the ELM in AI marketing contexts and extends the application of trust theory to human–AI interactions, offering valuable insights for future research on AI brand endorsement and consumer psychology.

Motivated by a deeply personal experience of losing his daughter, the author initiated this study to address the challenge of reconstructing personalized voices from low-quality, fragmented speech samples. The proposed Low-Quality Speech Reconstruction Method (LQSRM) integrates advanced noise reduction, spectral patching, and AI-based Text-to-Speech (TTS) and Speech-to-Speech (STS) technologies to restore coherent, speaker-specific voices. This innovative framework was evaluated using voice data from 10 speaker groups (5 male, 5 female), with a rigorous Mean Opinion Score (MOS) assessment involving 200 participants. Results demonstrated that LQSRM significantly outperformed conventional methods in naturalness and speaker similarity, providing a practical solution for memory preservation, assistive speech tools, and cultural heritage restoration.