首次用数据揭示朗读质量如何影响有声书吸引力
Audio-Based Understanding of Audiobook Narration Appeal
- 用预训练模型提取音色、语速等声学特征
- 声学特征与播放率相关，且独立于书名影响
- 为个性化推荐和主播选配提供数据支持
朗读是听众体验有声书的核心，直接影响其参与度与理解。本研究通过分析LibriVox中的音频特征（如音调、语速、音量）与消费数据（特别是观看率）的关系，探索朗读质量如何影响有声书吸引力，并考察其在不同体裁、书名和受众间的差异。尽管消费数据有限，我们发现仅凭声学信息即可稳健预测吸引力，且在控制书名效应后依然显著。进一步利用更精细的专有互动指标验证了结果。这是首个系统性地将朗读质量、体裁、书名与有声书消费关联起来的计算研究，凸显数据驱动方法在优化有声书个性化推荐与主播匹配方面的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。