INTEGRATING ACOUSTIC MODULATION AND LISTENER DEMOGRAPHICS FOR ENHANCED PODCAST EMOTIONAL RESONANCE
Examining the impact of voice modulation on audience response offers great promise in the transformation of speech media, providing key insights into digital content optimisation for emotional response and listener involvement. The research contradicts conventional views in previous studies by incl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Regional Association for Security and crisis management, Belgrade, Serbia
2025-04-01
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| Series: | Operational Research in Engineering Sciences: Theory and Applications |
| Subjects: | |
| Online Access: | https://www.oresta.org/menu-script/index.php/oresta/article/view/816 |
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| Summary: | Examining the impact of voice modulation on audience response offers great promise in the transformation of speech media, providing key insights into digital content optimisation for emotional response and listener involvement. The research contradicts conventional views in previous studies by including an overall framework for the improvement of auditory features in podcasts. The underlying framework combines analytical methods, computational modelling, and machine learning in order to systematically enhance audio quality and consequently increase listener engagement. In this regard, the study utilizes a mix of electrodermal response (EDR) measurements and listener feedback analysis as a basis for assessing emotional states prior to and following exposure to optimised voice modulation. Moreover, Random Forest (RF) and Long Short-Term Memory (LSTM) models are used to forecast audience mood, while Bayesian optimisation fine-tunes speech features to determine the most effective vocal characteristics. The research reveals that optimising storytelling parameters maximizes audience engagement to a large extent, resulting in a 14% boost in emotional scores and a 23% increase in interaction rates. This method not only enhances perception of the audience but also assesses the strength of the Acoustic Modulation for Emotion Optimisation (AMEO) model in speech conditioning. Unlike other studies that were mostly based on subjective surveys, this study takes a data-driven approach by combining physiological data with predictive modelling. Responding to the demand for better speech-content delivery, research here suggests an organized framework having real-world utility in audiobooks, human-computer communication, and smart assistants. Computational voice modulation finds its potential boosted through these conclusions to deliver further emotional impact and hence contribute towards acoustic optimisation. The research points to the revolutionary potential of data-driven voice modulation strategies, offering important lessons for creating more compelling and emotionally engaging digital media experiences.
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| ISSN: | 2620-1607 2620-1747 |