A Deeper Dive into StreamShark’s New Machine Learning Captioning Function

A Deeper Dive into StreamShark’s New Machine Learning Captioning Function

Machine learning based captioning is one of the hardest features to master in the events industry. However, as the amount of streaming VOD and pre-recorded video increases, the need for video captioning is more significant than ever.

The modern need for VOD captioning

Today, the need for captioning on videos is very important. In fact, it’s required by law.

In 1990, The Americans with Disabilities Act created accessibility requirements to protect citizens with disabilities from discrimination. Most are familiar with the ADA and its landmark legal requirements, making physical access to buildings available to everyone. However, the ADA also requires “auxiliary aids,” like captioning or audio descriptions, to be available to anyone with a disability.

Currently, closed captioning or audio transcriptions on videos are required for:

  • Both internal and external video communication, public entities: state and local governments.
  • Places of public accommodations: where the public at large uses public or private businesses. Private clubs and religious organizations are exempt.

In addition to making VODs accessible to everyone, there’s another significant reason we’re seeing the demand for captioning on videos rise.

Captioning on VOD makes it easier for viewers to understand what’s happening during a video presentation or meeting, especially when there are multiple speakers or presenters. 

While clear captioning and written transcriptions are highly important for user accessibility and comprehension, there are several significant issues with adding captions into enterprise video.

The current problems with video captioning

Historically, after a video conference or meeting, companies hire a human to listen to the video recording and manually transcribe the captions. Later, the captions can be added to the recorded video and offered as written transcription.

While human captioning is helpful, it’s far from efficient. First, human transcribers are expensive, especially when hiring a high-quality transcriber. Human transcribing can be a long, laborious process that delays adding captions to a video. This delay can slow the release of a recorded video, making important information outdated. Then, there’s the problem of human error. Humans are prone to inevitable mistakes or typos, which is a risk if a high quality captioner has not been engaged! There might be high profile live events where the expense of a high quality captioner for live captions is reasonable, however most internal meetings don’t necessitate live captions and captions after-the-fact are sufficient. That’s why we invented StreamShark’s brand new Machine Learning Captioning feature.

Introducing StreamShark’s Machine Learning Captioning Feature  

StreamShark’s machine generated captions aims to harness machine learning to seamlessly and quickly provide captioning to VODs as well as improve accuracy the more custom libraries are used (to train the underlying machine learning models) to help the smartest enterprises scale their communications.

The AI based machine generated captions automatically create captions for your recorded videos within minutes. In addition, the new Machine Learning Captioning is built into the current StreamShark platform, which makes for easy integration into existing organizational workflows.

How does the Machine Generated Captions Feature work?

Machine generated captioning is simple to use. To begin, users upload their video to the StreamShark platform and request automatic transcription. Shortly after uploading the video, the captions will be automatically added to VOD in less than five minutes. For longer, expect captions in about thirty minutes.

Once captions have been transcribed, the user has two options for receiving them. The first option is for StreamShark to automatically add the captions to the video without the user’s additional help or work. This is best for lower priority or less sensitive events, where a typo or two doesn’t matter quite so much.

The second option is for the user to download the caption transcription, fix any errors or remove sensitive information, then reload the edited captions onto the video. This method is best for highly sensitive VODs, where information may need to be removed, or for high-priority VODs, where perfection is the aim.

What are the unique features of Machine Learning Captioning?

The Machine Learning Captioning also features two unique functions which make this an exceptionally effective and responsive AI captioning service: 

  • English language support in 7 dialects
  • Customizable library of vocabulary

The first unique feature of the Machine Learning Captioning feature is that it currently supports the English language in a variety of 7 different dialects: American English, British English, Australian English, Indian English, Irish English, Scottish English, and Welsh English. This allows the Machining Learning Captioning function to be tailored to the presenter’s dialect and transcribe the most accurate captions possible. This is helpful in situations where common slang (i.e., brekkie for breakfast, as Australians might say) would otherwise confuse a human transcriber or ML application.

Additionally, while Machine Learning Captioning currently only supports the English language, StreamShark can provide transcribed captions in multiple languages. This is a significant benefit for large or international organizations wishing to deliver information from a video meeting or presentation verbally.

The second feature of StreamShark’s Machine Learning Captioning feature that makes it highly effective for organizations is the option to create a customizable library of vocabulary to help improve the accuracy of the captions. For example, a user could program in common industry abbreviations, slang terms, or even presenter names in order to most accurately reflect correct spelling and captioning during transcription. This customizable library function helps to improve comprehension for VOD viewers and is especially helpful for internal meetings with a lot of stakeholders.

In short? StreamShark’s Machine Learning Captioning is a lightning-quick, cost-effective way to add accurate captioning into recorded VODs and videos rapidly.

The future of Machine Learning Captioning

Captions for video will continue to be a huge need for organizations creating enterprise videos. 

That’s why we’re working on several exciting new features for StreamShark, including the ability for captioning to be automatically transcribed in multiple languages, including French, German, Chinese, Italian, Japanese, Korean, and Spanish.Additionally, StreamShark is developing the function to introduce automatic captioning into live stream events for even better viewer comprehension and engagement.

Get started with StreamShark today 

StreamShark is the trusted video platform that offers an end-to-end live and on-demand video streaming service, including automatic captioning with our new Machine Learning Captioning feature.

Get started with StreamShark today: Book a Demo

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