Automatic age recommendation system for children´s video content

This paper presents a novel automatic method to determine the appropriate age of video content in a video database geared to children. When combined with classical features the system improves accuracy rate for more than 0.13 for the same type of classifier in determining the age category of content for children between the ages of three to six years old. The main novelty of the system is that it utilizes high level audio features related to the cognitive capacity of children. These novel features gage the cognitive ability of the intended audience by quantifying the structure of the language. These novel features include syllable rate, word rate, language complexity and noise jumps. The feature extraction methods are also novel in that we count the number of syllables and words using relatively computationally inexpensive signal processing techniques forgoing complex speech recognition. The presented new method is tested using multiple classifiers on a commercial video database.