Audio-Based Understanding of Audiobook Narration Appeal
2026-07-02 • Computation and Language
Computation and LanguageSound
AI summaryⓘ
The authors studied how the way audiobooks are narrated affects how much people like and listen to them. They looked at features like voice tone, speed, and loudness from many audiobooks and compared these to how often the audiobooks were played. They found that these voice qualities are linked to how popular an audiobook is, even when considering the specific book or genre. This work is the first to connect narration style, book type, and listener behavior using data, which could help improve audiobook recommendations and narrator choices.
narrationaudiobookvocal featuresacoustic analysisgenrelistener engagementconsumption datapersonalizationLibriVox
Authors
Shahar Elisha, Mariano Beguerisse-Díaz, Emmanouil Benetos
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.