{"id":981,"date":"2022-09-29T13:05:51","date_gmt":"2022-09-29T17:05:51","guid":{"rendered":"https:\/\/courses.ideate.cmu.edu\/60-461\/f2022\/?p=981"},"modified":"2022-10-02T23:16:51","modified_gmt":"2022-10-03T03:16:51","slug":"the-female-voice-over-supercut","status":"publish","type":"post","link":"https:\/\/courses.ideate.cmu.edu\/60-461\/f2022\/rnav00-572\/09\/29\/the-female-voice-over-supercut\/","title":{"rendered":"The Female Voice-Over Supercut"},"content":{"rendered":"

This project stemmed from my continual interest in a common artistic choice of some of my favorite directors, who happen to be male: they chose to use a female voice to narrate their essay films. I’m curious about that choice, especially in the context of the essay film being a highly reflexive medium.<\/p>\n

I focused on three films by three directors, which I choose because they are among my favorites:<\/p>\n

\"\"<\/p>\n

I was loosely inspired by Omer Fast’s “CNN Concatenated”<\/a> and used Sam Lavigne’s Videogrep <\/a>\u00a0– a python-based tool that allows to automatically create supercuts, based on associated subtitles files. I also used the transcript software Descript<\/em> to make sure Videogrep doesn’t miss any excerpts since I knew the amateurish subtitles files I’m using might include some glitches.<\/p>\n

My rule was to only use supercuts of words that appeared in all three films (not including conjunctions). As a result, I had to let go of several words and supercuts I desperately wanted to use, since they only appeared in one or two of the films.<\/p>\n

I edited out clips where Videogrep mistakenly included words that sound similar to those I searched for (although I left the mistakes in if the words were of the same root).<\/p>\n

Here are the results arranged by four chosen words from all three films into one sequence:<\/p>\n