arXiv:2607.02473v1 Announce Type: new 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 in
The proliferation of audio content, particularly audiobooks, combined with advancements in AI for audio analysis, creates a fertile ground for understanding nuanced listener preferences.
This research provides insights into the qualitative factors that drive engagement in audio consumption, which could inform content creation, AI narration development, and platform curation strategies.
The explicit linking of acoustic features to consumption data allows for more data-driven approaches to optimizing audio content for audience appeal, potentially moving beyond subjective editorial choices.
- · Audiobook publishers
- · AI synthetic voice developers
- · Content creators
- · Audio analytics firms
- · Audioproduction houses resistant to data-driven insights
- · Amateur audiobook narrators without technical feedback
Improved recommendations and content quality in audio platforms based on AI-derived insights into narration appeal.
Increased demand for AI tools that can analyze or even generate nuanced vocal performances tailored to specific content and audience preferences.
A shift in the audiobook industry's talent landscape, where narrators may be coached or evaluated based on quantifiable 'appeal' metrics derived from AI analysis.
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