Tools for Automating the Captioning of Video

Tools for Automating the Captioning of Video 

Live Broadcast Date: Thursday, July 27, 2017
Time: 11:00 a.m. - 12:30 p.m. Eastern (UTC -- 4 hours)
Length: 1.5 hours
Speaker: Joseph 
Polizzotto
Target Audience: Alternate media specialists, video editors, transcribers, and anyone involved in the creation of captioned videos.
Skill Level: Intermediate 


Register for Tools for Automating the Captioning of Video

Overview

Creating synchronized video captions can be a time-consuming and tedious process. Thankfully, there are a variety of free to low-cost tools that speed up this process. In this session, we will demonstrate how to use speech recognition tools to produce a "raw" transcript and how to synchronize the transcript with a video. Finally we demonstrate the usefulness of Amara, which is a free, web-based portal you can use when you don't own the copyright to a video.


Many transcribers and video editors may experience a pain point in their production workflow when it comes to creation of a closed caption file (e.g., SRT, VTT, SBV). While many paid tools and services exist that can help with automatic synchronization of audio and text, why not consider using a fast and accurate tool that is open-source? And why not try to use speech recognition to quickly generate a transcript for an audio file while you're at it?

In this session, we will cover the following areas:

1. How do three zero to low-cost speech recognition tools stack up in accuracy? We will demonstrate and contrast the use of Dragon Naturally Speaking, Google Speech API, and IBM Watson's Speech to Text Service for creating a "raw" transcript of a video or audio recording.
 
2. What is a forced-alignment tool like Aeneas and how can this tool automatically synchronize a text transcript with an audio or video recording? We will discuss the use of the Aeneas Web App, a Python-based forced aligner that quickly produces SRT, VTT, and other syncmap formats when you already have a transcript and a clean audio file.
 
3. Basic steps for creating closed captioned YouTube videos using YouTube's native captioning tools. We will also discuss how to download the closed captioned file from YouTube for use within another video editing program.
 
4. When you need to caption a YouTube video that you don't own, what options do you have? We will demonstrate how to leverage YouTube's auto captions for videos you don't own and how to set up an Amara account to post a link to a captioned version of the YouTube video.
 
5. Finally, we will discuss tools (e.g., Captions Converter) that can be used to convert one caption file format to another (e.g., YouTube's' SBV to SRT)
 

Objectives:

  • Compare the accuracy of a few speech recognition tools, such as Dragon Naturally Speaking (with and without extensive testing), YouTube's Google API, and IBM Watson's Speech to Text Service, when creating a transcript of an audio or video file.
  • Tips and tricks for segmenting a "raw" transcript and how to download the auto-captions from YouTube.
  • Learn how to use the Aeneas Web App to automatically synchronize the audio from an audio or video file with a transcript.  Identify accessibility features of operating systems that can be used to evaluate functional access by users with disabilities.
  • Evaluate the accuracy of your closed captioning files (e.g., SRT, VTT, SBV).
  • Use Amara.org to share a captioned verson of a YouTube video you don't own.

Speaker Bio

Joseph Polizzotto is an access technology specialist instructor at the High Tech Center Training Unit (HTCTU) of the California Community Colleges, where he trains college faculty and staff on alternate media and assistive technology. His recent research interests include accessible EPUB 3 and mobile reading systems. Joseph graduated with a B.A. in History from UC Santa Cruz in 2000 and received an M.A. in TESOL from San José State University in 2004. 

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