How to Optimize Video Editing Platforms with ASR, NLP, and NLU Tools

Learn how Artificial Intelligence, Deep Learning, and Machine Learning backed tools–like Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Natural Language Understanding (NLU)--help create industry-best video editing platforms.

How to Optimize Video Editing Platforms with ASR, NLP, and NLU Tools

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Top YouTubers are making millions of dollars each year. B2C and B2B companies are also turning to this lucrative platform to raise brand awareness, nurture community engagement, and push subtle content marketing. On a larger scale, video is setting itself apart as the dominant communication medium–from TikTok to Instagram reels to virtual meetings to online learning classes.

Because of this explosive use, the market for video editing platforms is also seeing a significant increase in market investment. Top video editing platforms, or companies looking to build one, need to invest in powerful tools that differentiate them from competitors and drive innovation for end users.

This article examines how Artificial Intelligence, Deep Learning, and Machine Learning backed tools–like Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Natural Language Understanding (NLU)--are facilitating this process for industry-best video editing platforms.

Video Editing Platforms

Before we jump into ASR and NLP/NLU tools, let’s first look more closely at what a video editing platform is and what needs it seeks to fulfill.

What is a Video Editing Platform?

The goal of a video editing platform is to provide simple yet powerful online tools to ease the video editing process. It replaces clunky video editing software to help turn anyone into a content creator–from amateur YouTubers to classroom teachers to worldwide enterprises. This could include adding captions to videos, removing background noise, or adding visual effects, sound design, looping, cropping, compression, and more.

What are the Most Important Features of a Video Editing Platform?

For our purposes, we’ll be focusing on a few of the more intelligent, or AI-powered, features that really set today’s video editing platforms apart. These include those features that help users:

  • Eliminate the tedious manual review process of editing videos and make long videos more digestible for more efficient collaboration.
  • Quickly discover and surface important sections to create highlights and summaries.
  • Add captions for better accessibility and compliance, and create transcripts of videos for better searchability, indexing, and discovery.

Speech-to-Text and Audio Intelligence for Video Editing Platforms

ASR platforms, like Speech-to-Text APIs, transcribe video and audio streams automatically, like YouTube videos, into a highly accurate, readable transcription text. Historically, text was transcribed in large blocks, sans casing, punctuation, paragraphs, or speaker labels, making even accurate transcriptions hard to process.

However, today’s speech recognition models are built using cutting-edge AI, Deep Learning (DL), and Machine Learning (ML) research that can automatically transform these large blocks of text into something more user friendly. For example, AssemblyAI’s recently updated Automatic Casing and Punctuation Model is trained on text with billions of words, greatly improving transcription readability and utility.

Some Speech-to-Text APIs also go beyond transcription, offering advanced NLU/NLP tools referred to as Audio Intelligence APIs.

Using the latest ML, DL, and NLP research as a foundation, Audio Intelligence APIs let video editing platforms quickly build high ROI features and applications on top of their audio or video data. This could include detecting common entities, sorting a text automatically into chapters through summarization, and auto identifying and labeling each speaker in a video.

Let’s look more closely at the three biggest impacts Speech-to-Text and Audio Intelligence technology can have on video editing platforms:

1. Add Captions Automatically

First, today’s video editing platforms need to facilitate easy, accurate automatic audio transcription so end users can add accurate captions at the click of a button. This feature increases the accessibility of videos, whether it be for a personal YouTube video or a company Zoom meeting. According to the World Health Organization (WHO), 5% of people globally have a hearing impairment. Many organizations, universities, and others have compliance regulations in place to help meet the needs of this population, including necessitating captioning on all videos.

Captions also make it easier for viewers to watch and understand video, even when their sound is off. Sixty-nine percent of people view video without sound in public spaces; more than 25% do so in private spaces as well. Maybe even more compelling is that viewers are 80% more likely to watch a video with captions than one without. Video editing platforms need to make it easy to automatically add these accurate captions to videos so users can capitalize on these trends.

In addition, captions can also be used as “previews” that highlight a video’s content when potential viewers hover over the video, increasing user experience and engagement. Adding captions also makes videos more discoverable, and thus searchable, as search engines can crawl the text for relevant keywords. It even makes them more shareable–videos with captions are shared 15% more than those without captions.

If accuracy is a concern, make sure to find an API that offers Confidence Scores for its transcriptions. ASR technology is getting close to human-level accuracy, but it isn’t at 100% just yet. Typically, Confidence Scores work by providing a value from 0 to 100, with 0 being not accurate at all and 100 being perfect accuracy. If a platform or users needs a perfect, 100% accurate transcript, Confidence Scores drastically cut down on the time required to manually edit and review transcripts as areas of deficiency (or where there are lower confidence scores) can be noted and resolved with human transcription–without having to manually transcribe the entire audio stream over again.

Transcriptions can be also translated into a multitude of languages, for greater reach and utility.

2. Support Searchability and Indexing of Videos

While accurate transcription helps support basic searchability, incorporating additional Audio Intelligence features into video editing platforms can support searchability and indexing to a greater degree.

In this context, video editing platforms must be able to help users add:

  • Auto-tagging of video content with relevant tags. This categorizes videos and automates SEO, significantly improving search discoverability.
  • The ability to search across videos, create highlight videos (for meeting digests or social media posts), or review long videos in a few minutes.
  • Timestamps to add Tables of Contents or quickly scan through video segments.
  • Speaker labels to videos with multiple speakers.
  • Flags for the most important sections of videos.

There are a few Audio Intelligence features that can make this happen: Entity Detection, Auto Chapters/Summarization, and Speaker Diarization.

Entity Detection, also sometimes referred to as Named Entity Recognition, identifies and classifies key information in a transcription text. Common entities that can be detected include dates, email addresses, phone numbers, locations, occupations, and nationalities. Entity Detection can be used to create relevant tags for videos or to identify commonalities in video content.

Auto Chapters, or Summarization, provides a “summary over time” for audio streams. It works by (a) breaking a text into logical chapters, like where the conversation changes topics, and then (b) generating a short summary of each of these chapters. Auto chapters is an extremely useful tool that can help users quickly create Tables of Contents for YouTube or an online learning class, review videos in a short amount of time, or even create a summary of the most important video segments to push out internally or externally.

Speaker Diarization automatically applies speaker labels to a transcription text. Users can then add these to captions or video transcripts to ease readability for viewers.

3. Help Users Unlock Insights for Smarter Collaboration

Finally, video editing platforms must help users unlock key insights that boost video performance while simultaneously fostering smarter collaboration for involved parties.

The Audio Intelligence feature Auto Chapters makes it simple for users to share important video segments for manual review directly via the video editing platform or the cloud. Entity Detection tied to analytics can determine which video tags or filters are the most/least used, most/least popular, and more. Additional data can be aggregated to perform key analytics that help increase viewership, build communities, and lead to higher ROI.

Intelligent Video Editing Platforms

From social media to online learning platforms to Zoom meetings, video is quickly becoming the preferred method of personal and professional communication. Today’s video editing platforms must keep up with this shift by prioritizing intelligent technology that enhances the user experience.

ASR, NLP, and NLU tools serve as the foundational–and exemplary–tools that facilitate this transition and help the best video editing platforms set themselves apart from the competition.