Nedimo: An Evidence-Grounded Multilingual Pipeline for AI-Generated Sleep Stories

Authors: Georgios Makridis , Stefanos Kampouris

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Abstract: Generative audio systems can produce large libraries of personalized bedtime content, but ordinary media objectives (retention, completion, and novelty) are poorly aligned with the goal of supporting a transition toward sleep. Direct evidence for narrated bedtime audio in adults is mixed, and a recent active-controlled trial did not support such audio as a standalone treatment for sleep disturbance. We translate this uncertain evidence into conservative engineering requirements and present a multilingual pipeline for generating and narrating low-arousal sleep stories. The implemented prototype separates deterministic planning from large language model generation, represents content and delivery parameters explicitly, shapes each script through a four-phase attention taper, validates structural and lexical constraints, performs a bounded error-aware retry, and quarantines unresolved outputs. Provider-agnostic text-to-speech synthesis is followed by paragraph-aware joining, two-pass loudness normalization, and a publication gate that re-measures the saved, encoded file against fixed loudness, loudness-range, and true-peak limits. The product data model records stop position, replay, early abandonment, and completion while treating these events as ambiguous behavioral proxies rather than clinical outcomes. We contribute an evidence-to-requirement mapping, a traceable guardrail architecture with explicit acceptance thresholds, technical evaluation criteria, and a prospective protocol for testing sleep-onset effects. No clinical efficacy is claimed; the system is positioned as a relaxation and attentional-support tool whose benefit and possible reliance must be evaluated empirically.

Comments:

7 pages, 3 figures, 3 tables

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Sound (cs.SD)

Cite as:

arXiv:2610.40970 [cs.AI]

(or

arXiv:2610.40970v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.40970

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Georgios Makridis [ view email ]

[v1]

Thu, 29 Oct 2026 20:58:10 UTC (17 KB)

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