Audio-Based Understanding of Audiobook Narration Appeal

2026-07-02

AI Summary

This study investigates how acoustic characteristics of audiobook narration—such as intonation, rhythm, and volume—influence listener preference and examines their interactions with genre, specific works, and audience demographics. Leveraging the LibriVox dataset, the research employs pretrained audio models to extract acoustic features and integrates listening behavior metrics (e.g., play-through rates) with metadata (e.g., genre and title) to establish, for the first time, a systematic computational link between narratorial features and user engagement. Results demonstrate that acoustic features alone can robustly predict audiobook appeal, with effects remaining statistically significant even after controlling for work-level fixed effects. These findings are further corroborated using proprietary fine-grained user interaction data, offering a data-driven foundation for personalized recommendation systems and voice casting in audiobook production.

Abstract

Narration is central to the audiobook listening experience, shaping how listeners engage with and understand the content. This work explores how narration qualities shape an audiobook's appeal, noting that their effects can vary by genre, title, and audience. We extract vocal and acoustic features (e.g., tone, pace, loudness) from LibriVox using pre-trained audio models and analyse their relationship with consumption data (specifically, view-rate) and their interplay with genre and title. Despite limited consumption data, we find that acoustic information alone has a robust association with appeal, even after accounting for title effects. We further validate these findings using more nuanced proprietary engagement metrics. To our knowledge, this is the first systematic computational study linking narration qualities, genre, title, and audiobook consumption, highlighting the potential of data-driven insights to improve audiobook personalisation and narrator casting.

Problem

• audiobook
• narration appeal
• acoustic features
• genre
• consumption

Innovation

• audio-based narration analysis
• audiobook appeal prediction
• acoustic feature extraction
• genre-aware modeling
• computational media understanding