Low-Quality Speech Reconstruction for Deceased Voices: A Hybrid Approach Integrating Noise Reduction, Spectral Patching, and AI-Based TTS/STS Models

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.