Nedimo: An Evidence-Grounded Multilingual Pipeline for AI-Generated Sleep Stories
Organizations: Department of Digital Systems, University of Piraeus, Piraeus, Greece
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.
Figures & tables
| Study | Design and sample | Intervention | Implication for the present system |
|---|---|---|---|
| Harmat et al. [ 1 ] | Controlled study, students | 45 min/night for three weeks: music, audiobook, or control | Spoken content is not intrinsically sleep promoting; content and delivery require explicit design. |
| Kirk et al. [ 2 ] | Randomized within-person crossover, | Sleepcasts, music, mindfulness, and no-intervention weeks | Supports feasibility and possible physiological effects, but subjective benefit was not specific to stories. |
| Economides et al. [ 3 ] | Multi-arm pilot, working adults | App-based stories or music over four weeks | Supports scalable delivery and retention; pilot findings require active-controlled confirmation. |
| Mermer and Arslan [ 4 ] | Quasi-experimental intensive-care study, | One 30-min audiobook-plus-music session | Provides context-specific positive evidence but does not isolate narration or generalize to home use. |
| Vazzaz et al. [ 5 ] | Four-arm active-controlled RCT, | Stories, sounds, skills, or digital control for four weeks | No superiority on the primary outcome; motivates conservative claims and controlled evaluation. |
| Berglund et al. [ 6 ] | Cross-sectional self-selected online survey, Swedish adults | Self-reported bedtime audiobook use | Describes respondents’ motives, familiarity, and perceived reliance; cannot establish prevalence or efficacy. |
| Evidence or risk | Design requirement | Implemented control |
|---|---|---|
| Engaging, pleasant imagery can redirect unwanted pre-sleep thought [ 10 ] . | Capture attention without threat, urgency, or an unresolved goal. | Concrete opening, continuous low-stakes movement, sensory palette, limited dialogue, and bounded novelty. |
| Bedtime-story trials are mixed and the strongest active-controlled result is null [ 5 ] . | Do not represent the system as treatment or guarantee sleep. | Claim boundary in prompts, interface copy, validation rules, paper, and evaluation plan. |
| Daily-listening survey respondents more often reported reusing a book and difficulty falling asleep without audio [ 6 ] . | Support familiarity without engagement-maximizing habit mechanics. | Recurring-world mode, autoplay off, no streak objective, and reliance-oriented telemetry. |
| Environmental sound can disturb sleep; masking evidence is inconsistent [ 13 , 14 ] . | Keep level and dynamics controlled and ambience optional. | Voice-first mix, two-pass loudness normalization, post-encode loudness, loudness-range, and true-peak gate, and timed fade. |
| LLMs violate schemas and numerical length constraints [ 18 , 19 ] . | Verify rather than trust generation instructions. | Parseable front matter, server-computed word and taper budgets, deterministic validators, one targeted retry, then quarantine. |
| TTS realization varies across voices, languages, and providers [ 20 , 21 ] . | Separate narrative intent from synthesis configuration and waveform QA. | Provider adapter, paragraph chunks, locale settings, pronunciation metadata, silent join, and post-synthesis measurement. |
| Lang. | Rendered (min) | Error | LUFS | TP (dBTP) | LRA (LU) |
|---|---|---|---|---|---|
| en | 20.4 | 4.4 | |||
| de | 20.6 | 4.2 | |||
| fr | 17.4 | 4.4 | |||
| hi | 12.4 | 3.0 | |||
| ja | 15.2 | 4.4 |
Audio Generation
Audio generation creates speech, music, and sound from text, media, symbolic controls, or reference audio, with growing focus on controllability and realism.
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