Film Quality Prediction Using Acoustic, Prosodic and Lexical Cues
| Autor: | Alan Rozet, Su Ji Park |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Artificial neural network Computer science Speech recognition media_common.quotation_subject 05 social sciences 02 engineering and technology Variance (accounting) ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0502 economics and business 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Task analysis 050211 marketing Quality (business) ComputingMilieux_MISCELLANEOUS media_common |
| Zdroj: | SLT |
| DOI: | 10.1109/slt48900.2021.9383509 |
| Popis: | In this paper, we propose using acoustic, prosodic, and lexical features to identify movie quality as a decision support tool for film producers. Using a dataset of movie trailer audio clips paired with audience ratings for the corresponding film, we trained machine learning models to predict a film’s rating. We further analyze the impact of prosodic features with neural network feature importance approaches and find differing influence across genres. We finally compare acoustic, prosodic, and lexical feature variance against film rating, and find some evidence for an inverse association. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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