AI-Generated Voice, Synthetic Speech, and Voice Cloning: Scoping Review with ☸️SAIMSARA.

AI-generated voice is already being tested in education, healthcare, accessibility, media, and commercial interaction.

Voice-enabled AI can support patient education, clinical documentation, virtual patients, and medical simulations, but needs oversight.

Personalized voices may help users with visual, physical, hearing, or speech impairments preserve identity and communicate more naturally.

Listener studies showed weak or inconsistent detection, including very low accuracy in vishing-style synthetic voice clips.

Dataset-specific systems reported very high accuracy using spectrograms, acoustic features, CNNs, transformers, and ensemble models.

Prosody, pitch, timbre, spectral artifacts, vowel-level cues, and time-frequency anomalies remain important signals for detection.

Voice cloning raises practical questions about ownership, impersonation, misinformation, child-facing use, and posthumous or clinical identity replication.

Safe deployment depends on provenance, watermarking, authentication, explainable detection, and human review rather than one metric alone.

Generalizability is limited by small human studies, heterogeneous datasets, multilingual gaps, adversarial attacks, and real-time deployment constraints.